Regionalisation with Spatially Constrained Cluster Analysis

Published

December 5, 2022

case study : Regionalisation by Multivariate Water Point Attributes with Non-spatially Constrained and Spatially Constrained Clustering Methods.

1. OVERVIEW

Regionalisation with Spatially Constrained clustering analysis requires similar observations to be grouped according to their statistical attributes and spatial location.

This study focuses on regionalising analysis based on Nigeria’s water points attributes.

1.1 Objectives

Regionalise Nigeria by using the following measures :

  • Total number of water points by status, i.e. functional, non-functional, and unknown;

  • Percentage of water points by :

    • status (functional, non-functional, and unknown);

    • deployed water technology (hand pump, mechanical pump, stand tap, etc.) ;

    • usage capacity (1000, 300, 250, 50);

    • rural or urban.

1.2 Scope of Works

Some of the specific tasks for this study are :

  • import the shapefile into R with the appropriate sf method, and save it in a simple feature data frame format;

    • note : three (3) Projected Coordinate Systems of Nigeria are : EPSG: 26391, 26392, and 26303.

  • derive the proportion of functional and non-functional water points at LGA level (i.e. ADM2) by appropriate tidyr and dplyr methods;

  • combine geospatial and aspatial data frames into a simple feature data frame.

  • delineate water points measures functional regions by using :

    • conventional hierarchical clustering.

    • spatially constrained clustering algorithms.

  • plot two (2) main types of maps below :

  1. Thematic Mapping

    Show the derived water-point measures by appropriate statistical graphics and choropleth mapping technique.

  2. Analytical Mapping

    Plot delineated functional regions using non-spatially constrained and spatially constrained clustering algorithms.


2. R PACKAGE REQUIRED

Following are the packages require for this exercise :

2.1 Load R Packages into R Environment

Use the code chunk below.

pacman::p_load(sf, tidyverse, questionr, janitor, psych, ggplot2, gcookbook, tmap, ggpubr, corrplot, gtsummary, regclass, caret, heatmaply, ggdendro, cluster, factoextra, spdep, ClustGeo, GGally)


3. GEOSPATIAL DATA

3.1 Acquire Data Source

3.2 Import Aspatial Data

3.2.4 Create Master File

Usage of the code chunk below :

left_join( ) - dplyr - to combine wp_coord, wp_cond and wp_adm.

wp <- left_join((
  left_join(
    wp_coord, 
    wp_cond, 
    by = c("row_id")
    )),
  wp_adm, by = c("row_id"))

3.2.5 Convert Well Known Text (WKT) Data to SF Data Frame

  • The “New Georeferenced Column” in wp_rds contains spatial data in a WKT format.

  • Two (2) steps to convert the WKT data format into an sf data frame.

3.2.5.1 derive new field :: “geometry”

Usage of the code chunk below :

st_as_sfc( ) - sf - to derive a new field “geometry”.

wp$geometry = st_as_sfc(wp$`New Georeferenced Column`)

3.2.5.2 convert to SF Data Frame

Usage of the code chunk below :

st_sf( ) - sf - to convert the tibble data frame into sf data frame with crs first set to WGS 84 (EPSG : 4326).

st_crs( ) - sf - to retrieve coordinate reference system from the object.

wp_sf<- st_sf(wp, crs = 4326)
st_crs(wp_sf)
Coordinate Reference System:
  User input: EPSG:4326 
  wkt:
GEOGCRS["WGS 84",
    ENSEMBLE["World Geodetic System 1984 ensemble",
        MEMBER["World Geodetic System 1984 (Transit)"],
        MEMBER["World Geodetic System 1984 (G730)"],
        MEMBER["World Geodetic System 1984 (G873)"],
        MEMBER["World Geodetic System 1984 (G1150)"],
        MEMBER["World Geodetic System 1984 (G1674)"],
        MEMBER["World Geodetic System 1984 (G1762)"],
        MEMBER["World Geodetic System 1984 (G2139)"],
        ELLIPSOID["WGS 84",6378137,298.257223563,
            LENGTHUNIT["metre",1]],
        ENSEMBLEACCURACY[2.0]],
    PRIMEM["Greenwich",0,
        ANGLEUNIT["degree",0.0174532925199433]],
    CS[ellipsoidal,2],
        AXIS["geodetic latitude (Lat)",north,
            ORDER[1],
            ANGLEUNIT["degree",0.0174532925199433]],
        AXIS["geodetic longitude (Lon)",east,
            ORDER[2],
            ANGLEUNIT["degree",0.0174532925199433]],
    USAGE[
        SCOPE["Horizontal component of 3D system."],
        AREA["World."],
        BBOX[-90,-180,90,180]],
    ID["EPSG",4326]]

3.2.5.3 retrieve geometry summary :: wp_sf

Usage of the code chunk below :

st_geometry( ) - sf - to get the geometry summary.

st_geometry(wp_sf)
Geometry set for 95008 features 
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 2.707441 ymin: 4.301812 xmax: 14.21828 ymax: 13.86568
Geodetic CRS:  WGS 84
First 5 geometries:
POINT (6.95009 6.78599)
POINT (7.604793 6.78321)
POINT (7.60024 6.759284)
POINT (7.615451 6.799595)
POINT (7.65991 6.762375)

3.3 Import Boundary Data of Nigeria LGA

Usage of the code chunk below :

st_read( ) - sf - to read simple features.

select( ) - dplyr - to select “shapeName” variable.

bdy_nga <- st_read(dsn = "/jephOstan/ISSS624/class_project/project_2/data/geospatial",
               layer = "geoBoundaries-NGA-ADM2",
               crs = 4326) %>%
  select(shapeName)
Reading layer `geoBoundaries-NGA-ADM2' from data source 
  `D:\jephOstan\ISSS624\class_project\project_2\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 774 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS:  WGS 84
problems(bdy_nga)

3.3.1 Review Imported File

3.3.1.1 check for missing data

freq.na(bdy_nga$shapeName)
missing       % 
      0       0

3.3.1.2 check for duplication :: “shapeName”

Usage of the code chunk below :

duplicated( ) - base - to determine duplicate elements.

freq(duplicated(bdy_nga$shapeName))
        n    % val%
FALSE 768 99.2 99.2
TRUE    6  0.8  0.8

3.3.1.3 list the duplicated value :: “shapeName”

Usage of the code chunk below :

add_count( ) - dplyr - to count observations by group

wp_duplShapeName <- bdy_nga %>%
  add_count(bdy_nga$shapeName) %>%
  filter(n!=1) %>%
  select(-n)

wp_duplShapeName
Simple feature collection with 12 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 3.316459 ymin: 6.459038 xmax: 9.020704 ymax: 12.05035
Geodetic CRS:  WGS 84
First 10 features:
   shapeName bdy_nga$shapeName                       geometry
1      Bassa             Bassa MULTIPOLYGON (((6.708541 7....
2      Bassa             Bassa MULTIPOLYGON (((8.823522 10...
3   Ifelodun          Ifelodun MULTIPOLYGON (((4.664107 8....
4   Ifelodun          Ifelodun MULTIPOLYGON (((4.721977 7....
5   Irepodun          Irepodun MULTIPOLYGON (((5.05493 8.0...
6   Irepodun          Irepodun MULTIPOLYGON (((4.543349 7....
7   Nasarawa          Nasarawa MULTIPOLYGON (((8.554589 11...
8   Nasarawa          Nasarawa MULTIPOLYGON (((7.493228 8....
9        Obi               Obi MULTIPOLYGON (((8.191919 6....
10       Obi               Obi MULTIPOLYGON (((9.008576 8....

3.3.1.4 verify findings in section 3.3.1.3

Usage of the code chunk below :

tmap_mode( ) - tmap - to set tmap mode to static plotting or interactive.

tm_shape( ) - tmap - to specify the shape object.

tm_polygons( ) - tmap - to fill the polygons and draw the polygon borders.

tm_view( ) - tmap - to set the options for the interactive tmap viewer.

tm_fill( ) - tmap - to specify either which colour to be used or which data variable mapped to the colour palette.

tm_borders( ) - tmap - to draw the polygon borders.

tmap_style( ) - tmap - to set the tmap style.

tm_layout( ) - tmap - to set the layout of cartographic map.

tmap_mode("view")
tmap mode set to interactive viewing
tm_shape(bdy_nga)+
  tm_polygons()+
  tm_view(set.zoom.limits = c(6,8))+

tm_shape(wp_duplShapeName)+
  tm_fill("shapeName",
          n = 6,
          style = "jenks")+
  tm_borders(alpha = 0.5)+
  tmap_style("albatross")+
  tm_layout(main.title = "Distribution of Duplicated ShapeName",
            main.title.size = 1.3,
            main.title.position = "center")
tmap style set to "albatross"
other available styles are: "white", "gray", "natural", "cobalt", "col_blind", "beaver", "bw", "classic", "watercolor"

Remarks :

The plot above indicates those duplicated water points are from different Nigeria states.

tmap_mode("plot")
tmap mode set to plotting

3.3.1.5 acquire State info for duplicated value

The State info to be combined with the duplicated “shapeName”. This will make all the shapeName unique.

lga row_id headquarter state iso3166code state_dd_coordinates
Bassa 94 Oguma Kogi NG.KO.BA 7.75 6.75
Bassa 95 Bassa Plateau NG.PL.BA 9.16667 9.75
Ifelodun 304 Share Kwara NG.KW.IF 8.5 5.0
Ifelodun 305 Ikirun Osun NG.OS.ID 7.5 4.5
Irepodun 355 Omu Aran Kwara NG.KW.IR 8.5 5.0
Irepodun 356 Ilobu Osun NG.OS.IP 7.5 4.5
Nasarawa 519 Bompai Kano NG.KN.NA 11.5 8.5
Nasarawa 520 Nasarawa Nasarawa NG.NA.NA 8.53 7.7
Obi 546 Obi Nasarawa NG.NA.OB 8.53 7.7
Obi 547 Obarike-Ito Benue NG.BE.OB 7.33333 8.75
Surelere 693 Surelere Lagos NG.LA.SU 6.5 3.35
Surelere 694 Iresa-Adu Oyo NG.OY.SU 8.07 4.41


3.4 Data Wrangling

3.4.1 Edit Duplicated Value :: “shapeName”

bdy_nga$shapeName[c(94,95,304,305,355,356,519,546,547,693,694)] <- 
  c("Bassa Kogi",
    "Bassa Plateau",
    "Ifelodun Kwara",
    "Ifelodun Osun",
    "Irepodun Kwara",
    "Irepodun Osun",
    "Nasarawa Kano",
    "Nasarawa Nasarawa",
    "Obi Nasarawa",
    "Obi Benue",
    "Surulere Lagos",
    "Surulere Oyo")
Warning in bdy_nga$shapeName[c(94, 95, 304, 305, 355, 356, 519, 546, 547, :
number of items to replace is not a multiple of replacement length

3.4.1.1 validate edited value :: “shapeName”

wp_duplShapeName1 <- bdy_nga %>%
  add_count(bdy_nga$shapeName) %>%
  filter(n!=1) %>%
  select(-n)

wp_duplShapeName1
Simple feature collection with 0 features and 2 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Geodetic CRS:  WGS 84
[1] shapeName         bdy_nga$shapeName geometry         
<0 rows> (or 0-length row.names)

3.4.2 Perform Point-in-Polygon Overlay

This step combine both attribute and boundary of the water points into a simple feature object.

3.4.2.1 join Objects :: wp_sf and bdy_nga

Usage of the code chunk below :

st_join( ) - sf - to join sf-class objects based on geometry, namely, wp_sf and bdy_nga.

wp_joined <- st_join(wp_sf, bdy_nga)

3.4.2.2 save and read RDS File :: wp_joined

write_rds(wp_joined,"/jephOstan/ISSS624/class_project/project_2/data/geodata/wp_joined.rds",compress = "xz")

wp_joined <- read_rds("/jephOstan/ISSS624/class_project/project_2/data/geodata/wp_joined.rds")

3.4.2.3 inspect joined file :: wp_joined

st_geometry(wp_joined)
Geometry set for 95008 features 
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 2.707441 ymin: 4.301812 xmax: 14.21828 ymax: 13.86568
Geodetic CRS:  WGS 84
First 5 geometries:
POINT (6.95009 6.78599)
POINT (7.604793 6.78321)
POINT (7.60024 6.759284)
POINT (7.615451 6.799595)
POINT (7.65991 6.762375)

-- determine reference point :: “shapeName” or “clean_adm2”

wp_refLga <- (wp_joined$shapeName == wp_joined$clean_adm2)
freq(wp_refLga)
          n    % val%
FALSE 29713 31.3 31.3
TRUE  65266 68.7 68.7
NA       29  0.0   NA

Remarks :

There are 29,713 of “FALSE”, which is more than 30% of local government areas’ name mismatched between “shapeName” and “clean_adm2”.

  • Unlike the Water Point Data Exchange data that involved multitple parties, the geoBoundaries data is sourced from “geoBoundaries: A global database of political administrative boundaries.” Plos one 15, no. 4 (2020): e0231866,

    • Hence, the “shapeName” to be used throughout this study.

-- assess uniqueness of each Water Point

wp_joined %>% janitor::get_dupes(shapeName,lat_lon_deg)
No duplicate combinations found of: shapeName, lat_lon_deg
Simple feature collection with 0 features and 24 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Geodetic CRS:  WGS 84
# A tibble: 0 × 25
# … with 25 variables: shapeName <chr>, lat_lon_deg <chr>, dupe_count <int>,
#   row_id <dbl>, lat_deg <dbl>, lon_deg <dbl>, New Georeferenced Column <chr>,
#   water_source <chr>, water_source_clean <chr>, water_source_category <chr>,
#   water_tech_clean <chr>, water_tech_category <chr>, status_clean <chr>,
#   status <chr>, clean_adm1 <chr>, clean_adm2 <chr>,
#   water_point_population <dbl>, local_population_1km <dbl>,
#   crucialness_score <dbl>, pressure_score <dbl>, usage_capacity <dbl>, …

Remarks :

Each water point observation is unique as there are no duplicate combination of “shapeName” together with “lat_lon_deg”.

-- reveal value :: “status_clean”

freq(wp_joined$status_clean)
                                     n    % val%
Abandoned                          175  0.2  0.2
Abandoned/Decommissioned           234  0.2  0.3
Functional                       45883 48.3 54.4
Functional but needs repair       4579  4.8  5.4
Functional but not in use         1686  1.8  2.0
Non-Functional                   29385 30.9 34.8
Non-Functional due to dry season  2403  2.5  2.8
Non functional due to dry season     7  0.0  0.0
NA                               10656 11.2   NA

-- reveal value :: “crucialness_score”

summary(wp_joined$crucialness_score)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   0.130   0.304   0.414   0.628   1.000    6879

-- reveal value :: “is_urban”

freq(wp_joined$is_urban)
          n    % val%
FALSE 75444 79.4 79.4
TRUE  19564 20.6 20.6

-- reveal value :: “water_tech_category”

freq(wp_joined$water_tech_category)
                    n    % val%
Hand Pump       58755 61.8 69.2
Mechanized Pump 25644 27.0 30.2
Rope and Bucket     1  0.0  0.0
Tapstand          553  0.6  0.7
NA              10055 10.6   NA

-- reveal value :: “usage_capacity”

freq(wp_joined$usage_capacity)
         n    % val%
50       2  0.0  0.0
250    573  0.6  0.6
300  68789 72.4 72.4
1000 25644 27.0 27.0

3.4.3 Replace “NA” with “Unknown”

mutate( ) - dplyr - to run replace_na( ) function.

  • replace_na( ) - tidyr - to replace NAs with “unknown”.
wp_joined1 <- wp_joined %>%
  mutate(status_clean = replace_na(status_clean, "Unknown")) %>%
  mutate(water_tech_category = replace_na(water_tech_category, "Unknown")) %>%
  mutate(status = replace_na(status, "Unknown")) %>%
  mutate(water_point_population = replace_na(water_point_population, 0)) %>%
  mutate(local_population_1km = replace_na(local_population_1km, 0)) %>%
  mutate(crucialness_score = replace_na(crucialness_score, 0)) %>%
  mutate(pressure_score = replace_na(pressure_score, 0))

3.4.4 Standardise Value

3.4.4.1 combine value :: “status_clean”

wp_joined1 <- wp_joined1 %>%
  mutate(status_clean = str_replace(status_clean,"Non functional due to dry season"  ,"Non-Functional due to dry season")) %>%
  mutate(status_clean = str_replace(status_clean,"Abandoned/Decommissioned/Decommissioned","Abandoned/Decommissioned"))

3.4.4.2 review “status_clean”

freq(wp_joined1$status_clean)

3.4.4.3 read RDS file :: wp_joined1

wp_joined1 <- read_rds("/jephOstan/ISSS624/class_project/project_2/data/geodata/wp_joined1.rds")

3.4.5 Extract Water Point for New Table :: wp_nga

3.4.5.1 extract functional water point

wpt_functional <- wp_joined1 %>%
  filter(status_clean %in%
           c("Functional", 
             "Functional but not in use",
             "Functional but needs repair"))

-- save and read RDS file :: wpt_functional

write_rds(wpt_functional,"/jephOstan/ISSS624/class_project/project_2/data/geodata/wpt_functional.rds",compress = "xz")

wpt_functional <- read_rds("/jephOstan/ISSS624/class_project/project_2/data/geodata/wpt_functional.rds")

3.4.5.2 inspect variable and value

-- reveal value :: “status_clean”

freq(wpt_functional$status_clean)
                                n    % val%
Functional                  45883 88.0 88.0
Functional but needs repair  4579  8.8  8.8
Functional but not in use    1686  3.2  3.2
length(wpt_functional$row_id)
[1] 52148
length(wpt_functional$row_id)/length(wp_joined1$row_id)*100
[1] 54.88801

Remarks :

The total functional water points is 52,148 which is about 54.89% of total water points.

-- reveal value :: “usage_capacity”

freq(wpt_functional$usage_capacity)
         n    % val%
50       2  0.0  0.0
250     75  0.1  0.1
300  38064 73.0 73.0
1000 14007 26.9 26.9

-- reveal value “usage_capacity” by “status_clean”

wpt_functional %>% count(status_clean, usage_capacity, sort = TRUE)
Simple feature collection with 10 features and 3 fields
Geometry type: MULTIPOINT
Dimension:     XY
Bounding box:  xmin: 2.711632 ymin: 4.302938 xmax: 13.5022 ymax: 13.86331
Geodetic CRS:  WGS 84
# A tibble: 10 × 4
   status_clean                usage_capacity     n                     geometry
 * <chr>                                <dbl> <int>             <MULTIPOINT [°]>
 1 Functional                             300 33687 ((3.064921 7.994882), (3.06…
 2 Functional                            1000 12124 ((3.080189 7.99252), (3.085…
 3 Functional but needs repair            300  3306 ((3.340832 8.037962), (3.34…
 4 Functional but needs repair           1000  1271 ((3.373801 7.992051), (3.33…
 5 Functional but not in use              300  1071 ((3.046639 8.017765), (2.88…
 6 Functional but not in use             1000   612 ((3.088655 8.005296), (3.05…
 7 Functional                             250    70 ((3.355785 6.498105), (3.67…
 8 Functional but not in use              250     3 ((8.032945 6.878883), (7.00…
 9 Functional                              50     2 ((7.027967 4.765731), (8.92…
10 Functional but needs repair            250     2 ((6.465915 5.826699), (7.93…

-- reveal value :: “crucialness_score”

summary(wpt_functional$crucialness_score == 1)
   Mode   FALSE    TRUE 
logical   47006    5142

-- determine the total population within 1 km by “crucialness_score”

freq(wpt_functional$crucialness_score == 1)
          n    % val%
FALSE 47006 90.1 90.1
TRUE   5142  9.9  9.9
sum(wpt_functional[wpt_functional$crucialness_score == 1,]$local_population_1km)
[1] 11252574

Remarks :

Given the “crucialness_score” is a ratio of current water point users to the total population within 1 km radius thereof :

  • Currently, 5,142 water points serve the population within a 1 km radius at its capacity limit.

    • The usage capacity may need to be increased to sustain or improve the growth or development rate within 1km of these water points.

    • Should the population within 1 km therefrom grow above 11,252,574, there may be multiple repercussions in resources management, urbanisation progress, local food and beverage consumption, local commodity prices, or worst case scenario would be the stability of civil society.

summary(wpt_functional$pressure_score > 1)
   Mode   FALSE    TRUE 
logical   27469   24679 
length(wpt_functional$pressure_score)
[1] 52148
24679/52148*100 #percentage of functional waterpoints over their usage limit
[1] 47.32492

Remarks :

Given the “pressure_score” is the ratio of the current water point users to the usage capacity thereof :

  • 24,679, or 47.32% of functional water points, are currently over their limit of usage capacity.

3.4.5.3 Exploratory Data Analysis (EDA) :: wpt_functional

-- plot “status_clean”

ggplot(data = wpt_functional,
       aes(fct_infreq(status_clean), fill=status_clean))+ 
  geom_bar()+
  geom_text(
     aes(label=after_stat(count)),
     stat='count',
     nudge_x=-0.25,
     vjust=-0.2)+
  geom_text(
     aes(label= scales::percent(signif(after_stat(count/sum(count))))),
     stat='count',
     nudge_x=0.25,
     vjust=-0.2)+
  scale_x_discrete(guide = guide_axis(n.dodge = 2))+
  guides(fill=guide_legend (title = 'Status'))

-- plot “water_tech_category”

ggplot(data=wpt_functional, 
       aes(x=fct_infreq(
         water_tech_category)))+
  geom_bar(aes(
    fill = water_tech_category), 
    width = 0.8)+
  geom_text(aes(
    label = ..count..),
    stat = "count", 
    vjust=-0.2, 
    size = 3.5, 
    color = "black")+
  scale_x_discrete(guide = guide_axis(n.dodge = 2))+
  guides(fill=guide_legend (title = 'Water Tech Deployed'))
Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(count)` instead.

-- plot “water_source_clean”

ggplot(data=wpt_functional, 
       aes(x=fct_infreq(
         water_source_clean)))+
  geom_bar(aes(
    fill = water_source_clean), 
    width = 0.8)+
  geom_text(aes(
    label = ..count..),
    stat = "count", 
    vjust=-0.2, 
    size = 3.5, 
    color = "black")+
  scale_x_discrete(guide = guide_axis(
    n.dodge = 2))+
  guides(fill=guide_legend (
    title = 'Source of Water Supply'))

3.4.5.4 add attribute to new data table

wp_nga <- bdy_nga %>%
  mutate(`total_wp` = lengths(
    st_intersects(bdy_nga, wp_joined1))) %>%
  
  mutate(`wp_functional` = lengths(
    st_intersects(bdy_nga, wpt_functional))) %>%
  
  mutate(`pct_functional` = (`wp_functional`/`total_wp`*100))

-- replace “NaN” with 0

wp_nga <- wp_nga %>%
  mutate(`pct_functional` = replace_na(pct_functional, 0))

summary(wp_nga)
  shapeName                  geometry      total_wp     wp_functional   
 Length:774         MULTIPOLYGON :774   Min.   :  0.0   Min.   :  0.00  
 Class :character   epsg:4326    :  0   1st Qu.: 45.0   1st Qu.: 17.00  
 Mode  :character   +proj=long...:  0   Median : 96.0   Median : 45.50  
                                        Mean   :122.7   Mean   : 67.36  
                                        3rd Qu.:168.8   3rd Qu.: 87.75  
                                        Max.   :894.0   Max.   :752.00  
 pct_functional  
 Min.   :  0.00  
 1st Qu.: 32.61  
 Median : 47.41  
 Mean   : 49.84  
 3rd Qu.: 66.99  
 Max.   :100.00

3.4.5.5 extract non-functional water point

wpt_nonFunctional <- wp_joined1 %>%
  filter(status_clean %in%
           c("Abandoned/Decommissioned", 
             "Non-Functional",
             "Non-Functional due to dry season"))

-- save and read RDS file :: wpt_nonFuntional

write_rds(wpt_nonFunctional,"/jephOstan/ISSS624/class_project/project_2/data/geodata/wpt_nonFunctional.rds",compress = "xz")

wpt_nonFunctional <- read_rds("/jephOstan/ISSS624/class_project/project_2/data/geodata/wpt_nonFunctional.rds")

3.4.5.6 inspect variable and value

-- reveal value :: “status_clean”

freq(wpt_nonFunctional$status_clean)
                                     n    % val%
Abandoned/Decommissioned           234  0.7  0.7
Non-Functional                   29385 91.7 91.7
Non-Functional due to dry season  2410  7.5  7.5
length(wpt_nonFunctional$row_id)
[1] 32029
length(wpt_nonFunctional$row_id)/length(wp_joined1$row_id)*100
[1] 33.7119

Remarks :

There are 32,204, which is about 33.9% out of total water points.

-- reveal value :: “usage_capacity”

freq(wpt_nonFunctional$usage_capacity)
         n    % val%
250     41  0.1  0.1
300  20586 64.3 64.3
1000 11402 35.6 35.6

-- reveal value “usage_capacity” by “status_clean”

wpt_nonFunctional %>% count(status_clean, usage_capacity, sort = TRUE)
Simple feature collection with 7 features and 3 fields
Geometry type: MULTIPOINT
Dimension:     XY
Bounding box:  xmin: 2.707441 ymin: 4.301812 xmax: 13.4192 ymax: 13.86567
Geodetic CRS:  WGS 84
# A tibble: 7 × 4
  status_clean                     usage_capac…¹     n                  geometry
* <chr>                                    <dbl> <int>          <MULTIPOINT [°]>
1 Non-Functional                             300 18492 ((3.064526 7.994448), (3…
2 Non-Functional                            1000 10852 ((3.083391 7.993231), (3…
3 Non-Functional due to dry season           300  2012 ((3.051752 7.984243), (3…
4 Non-Functional due to dry season          1000   398 ((3.056661 7.985808), (3…
5 Abandoned/Decommissioned                  1000   152 ((4.713438 7.891137), (4…
6 Abandoned/Decommissioned                   300    82 ((3.199483 8.912549), (2…
7 Non-Functional                             250    41 ((3.976195 6.582998), (3…
# … with abbreviated variable name ¹​usage_capacity

-- reveal “crucialness_score”

sum(wpt_nonFunctional$local_population_1km)
[1] 93999535
sum(wpt_nonFunctional$water_point_population)
[1] 46255888

Remarks :

Given the “crucialness_score” is a ratio of current water point users to the total population within a 1 km radius thereof , in the context of non-functional :

  • Currently, out of 95,013,340 residents within a 1 km radius, there are 46,710,127 of them is affected by these non-functional water point.

3.4.5.7 EDA :: wpt_nonFunctional

-- plot “status_clean”

ggplot(data = wpt_nonFunctional,
       aes(fct_infreq(status_clean), 
           fill=status_clean))+ 
  geom_bar()+
  geom_text(
     aes(label=after_stat(count)),
     stat='count',
     nudge_x=-0.25,
     vjust=-0.2)+
  geom_text(
     aes(label= scales::percent(
       signif(
         after_stat(
           count/sum(count)
           )))),
     stat='count',
     nudge_x=0.25,
     vjust=-0.2)+
  scale_x_discrete(
    guide = guide_axis(
      n.dodge = 2))+
  guides(fill=guide_legend (
    title = 'Status'))

-- plot “water_tech_category”

ggplot(data=wpt_nonFunctional, 
       aes(fct_infreq(
         water_tech_category)))+
  geom_bar(aes(
    fill = water_tech_category), 
    width = 0.8)+
  geom_text(aes(
    label = ..count..),
    stat = "count",
    vjust=-0.2,
    size = 3.5,
    color = "black")+
  scale_x_discrete(guide = guide_axis(
    n.dodge = 2))+
  guides(fill=guide_legend (
    title = 'Water Tech Deployed'))

-- plot “water_source_clean”

ggplot(data=wpt_nonFunctional, 
       aes(fct_infreq(
         water_source_clean)))+
  geom_bar(aes(
    fill = water_source_clean),
    width = 0.8)+
  geom_text(aes(
    label = ..count..),
    stat = "count",
    vjust=-0.2,
    size = 3.5,
    color = "black")+
  scale_x_discrete(guide = guide_axis(
    n.dodge = 2))+
  guides(fill=guide_legend (
    title = 'Source of Water Supply'))

3.4.5.8 add wpt_nonFunctional to wp_nga

wp_nga <- wp_nga %>%
  mutate(`wp_nonFunctional` = lengths(
    st_intersects(bdy_nga, wpt_nonFunctional))) %>%
  mutate(`pct_nonFunctional` = (`wp_nonFunctional`/`total_wp`*100))

-- replace “NaN” with 0

wp_nga <- wp_nga %>%
  mutate(`pct_nonFunctional` = replace_na(pct_nonFunctional, 0))

summary(wp_nga)
  shapeName                  geometry      total_wp     wp_functional   
 Length:774         MULTIPOLYGON :774   Min.   :  0.0   Min.   :  0.00  
 Class :character   epsg:4326    :  0   1st Qu.: 45.0   1st Qu.: 17.00  
 Mode  :character   +proj=long...:  0   Median : 96.0   Median : 45.50  
                                        Mean   :122.7   Mean   : 67.36  
                                        3rd Qu.:168.8   3rd Qu.: 87.75  
                                        Max.   :894.0   Max.   :752.00  
 pct_functional   wp_nonFunctional pct_nonFunctional
 Min.   :  0.00   Min.   :  0.00   Min.   :  0.00   
 1st Qu.: 32.61   1st Qu.: 12.00   1st Qu.: 20.77   
 Median : 47.41   Median : 33.50   Median : 34.89   
 Mean   : 49.84   Mean   : 41.37   Mean   : 35.58   
 3rd Qu.: 66.99   3rd Qu.: 60.00   3rd Qu.: 50.00   
 Max.   :100.00   Max.   :278.00   Max.   :100.00

3.4.5.9 extract unknown water point

wpt_unknown <- wp_joined1 %>%
  filter(status_clean == "Unknown")

-- save and read RDS file :: wpt_unknown

write_rds(wpt_unknown,"/jephOstan/ISSS624/class_project/project_2/data/geodata/wpt_unknown.rds")

wpt_unknown <- read_rds("/jephOstan/ISSS624/class_project/project_2/data/geodata/wpt_unknown.rds")

3.4.5.10 inspect variable and value

-- reveal value :: “status_clean”

length(wpt_unknown$row_id)
[1] 10656
length(wpt_unknown$row_id)/length(wp_joined1$row_id)*100
[1] 11.2159

Remarks :

There are 10,656 water points with unknown status, about 11.22% of total water points.

-- determine affected population

sum(wpt_unknown$water_point_population)
[1] 18831488
sum(wpt_unknown$local_population_1km)
[1] 31418651

3.4.5.11 add wpt_unknown to wp_nga

wp_nga <- wp_nga %>%
  mutate(`wp_unknown` = lengths(
    st_intersects(bdy_nga, wpt_unknown))) %>%
  mutate(`pct_unknown` = (`wp_unknown`/`total_wp`*100))

-- replace “NaN” with 0

wp_nga <- wp_nga %>%
  mutate(`pct_unknown` = replace_na(pct_unknown, 0))

summary(wp_nga)
  shapeName                  geometry      total_wp     wp_functional   
 Length:774         MULTIPOLYGON :774   Min.   :  0.0   Min.   :  0.00  
 Class :character   epsg:4326    :  0   1st Qu.: 45.0   1st Qu.: 17.00  
 Mode  :character   +proj=long...:  0   Median : 96.0   Median : 45.50  
                                        Mean   :122.7   Mean   : 67.36  
                                        3rd Qu.:168.8   3rd Qu.: 87.75  
                                        Max.   :894.0   Max.   :752.00  
 pct_functional   wp_nonFunctional pct_nonFunctional   wp_unknown    
 Min.   :  0.00   Min.   :  0.00   Min.   :  0.00    Min.   :  0.00  
 1st Qu.: 32.61   1st Qu.: 12.00   1st Qu.: 20.77    1st Qu.:  0.00  
 Median : 47.41   Median : 33.50   Median : 34.89    Median :  0.00  
 Mean   : 49.84   Mean   : 41.37   Mean   : 35.58    Mean   : 13.76  
 3rd Qu.: 66.99   3rd Qu.: 60.00   3rd Qu.: 50.00    3rd Qu.: 17.75  
 Max.   :100.00   Max.   :278.00   Max.   :100.00    Max.   :219.00  
  pct_unknown    
 Min.   :  0.00  
 1st Qu.:  0.00  
 Median :  0.00  
 Mean   : 12.55  
 3rd Qu.: 20.83  
 Max.   :100.00

3.4.5.12 visualise distribution :: “status_clean”

Usage of the code chunk below :

qtm( ) - tmap - to plot a thematic map quickly.

tmap_arrange( ) - tmap - to arrange small multiples in grid layout.

total_wp <- qtm(wp_nga,"total_wp")+
  tm_layout(legend.height = 0.3, legend.width = 0.5)

wp_functional <- qtm(wp_nga,"wp_functional")+
  tm_layout(legend.height = 0.3, legend.width = 0.5)

wp_nonFunctional <- qtm(wp_nga,"wp_nonFunctional")+
  tm_layout(legend.height = 0.3, legend.width = 0.5)

wp_unknown <- qtm(wp_nga,"wp_unknown")+
  tm_layout(legend.height = 0.3, legend.width = 0.5)

tmap_arrange(total_wp, wp_functional, wp_nonFunctional, wp_unknown, asp=0, ncol = 2, nrow = 2, widths = 5, heights = 10, sync = TRUE)

3.4.5.13 extract “water_tech_category” to wp_nga

freq(wp_joined1$water_tech_category, sort = "dec")
                    n    % val%
Hand Pump       58755 61.8 61.8
Mechanized Pump 25644 27.0 27.0
Unknown         10055 10.6 10.6
Tapstand          553  0.6  0.6
Rope and Bucket     1  0.0  0.0

Remarks :

Only “Hand Pump”, “Mechanized Pump”, and “Tapstand” are to be extracted for further analysis as the rest are either less than 0.5% or “Unknown”.

wtc_hPump <- wp_joined1 %>%
  filter(water_tech_category %in%
           "Hand Pump")

wtc_mPump <- wp_joined1 %>%
  filter(water_tech_category %in%
           "Mechanized Pump")

wtc_tStand <- wp_joined1 %>%
  filter(water_tech_category %in%
           "Tapstand")

wp_nga <- wp_nga %>%
  mutate(`total_handPump` = lengths(
    st_intersects(bdy_nga, wtc_hPump)
  )) %>%
  mutate(`total_mechPump` = lengths(
    st_intersects(bdy_nga, wtc_mPump)
  )) %>%
    mutate(`total_tapStand` = lengths(
    st_intersects(bdy_nga, wtc_tStand)
  )) %>%
  mutate(`pct_handPump` = (`total_handPump`/`total_wp`*100)) %>%
  mutate(`pct_mechPump` = (`total_mechPump`/`total_wp`*100)) %>%
  mutate(`pct_tapStand` = (`total_tapStand`/`total_wp`*100))

-- replace “NaN” with 0

wp_nga <- wp_nga %>%
  mutate(`pct_handPump` = replace_na(pct_handPump, 0)) %>%
  mutate(`pct_mechPump` = replace_na(pct_mechPump, 0)) %>%
  mutate(`pct_tapStand` = replace_na(pct_tapStand, 0))

summary(wp_nga)
  shapeName                  geometry      total_wp     wp_functional   
 Length:774         MULTIPOLYGON :774   Min.   :  0.0   Min.   :  0.00  
 Class :character   epsg:4326    :  0   1st Qu.: 45.0   1st Qu.: 17.00  
 Mode  :character   +proj=long...:  0   Median : 96.0   Median : 45.50  
                                        Mean   :122.7   Mean   : 67.36  
                                        3rd Qu.:168.8   3rd Qu.: 87.75  
                                        Max.   :894.0   Max.   :752.00  
 pct_functional   wp_nonFunctional pct_nonFunctional   wp_unknown    
 Min.   :  0.00   Min.   :  0.00   Min.   :  0.00    Min.   :  0.00  
 1st Qu.: 32.61   1st Qu.: 12.00   1st Qu.: 20.77    1st Qu.:  0.00  
 Median : 47.41   Median : 33.50   Median : 34.89    Median :  0.00  
 Mean   : 49.84   Mean   : 41.37   Mean   : 35.58    Mean   : 13.76  
 3rd Qu.: 66.99   3rd Qu.: 60.00   3rd Qu.: 50.00    3rd Qu.: 17.75  
 Max.   :100.00   Max.   :278.00   Max.   :100.00    Max.   :219.00  
  pct_unknown     total_handPump   total_mechPump   total_tapStand   
 Min.   :  0.00   Min.   :  0.00   Min.   :  0.00   Min.   : 0.0000  
 1st Qu.:  0.00   1st Qu.:  6.00   1st Qu.: 11.00   1st Qu.: 0.0000  
 Median :  0.00   Median : 47.00   Median : 25.50   Median : 0.0000  
 Mean   : 12.55   Mean   : 75.89   Mean   : 33.12   Mean   : 0.7145  
 3rd Qu.: 20.83   3rd Qu.:111.00   3rd Qu.: 46.00   3rd Qu.: 0.0000  
 Max.   :100.00   Max.   :764.00   Max.   :245.00   Max.   :42.0000  
  pct_handPump     pct_mechPump     pct_tapStand    
 Min.   :  0.00   Min.   :  0.00   Min.   : 0.0000  
 1st Qu.: 16.70   1st Qu.: 12.20   1st Qu.: 0.0000  
 Median : 50.99   Median : 31.27   Median : 0.0000  
 Mean   : 48.73   Mean   : 37.54   Mean   : 0.5794  
 3rd Qu.: 77.78   3rd Qu.: 57.71   3rd Qu.: 0.0000  
 Max.   :100.00   Max.   :100.00   Max.   :32.8947

3.4.5.14 visualise wp_nga distribution :: “water_tech_category”

tmap_mode("view")
tmap mode set to interactive viewing
handPump <- tm_shape(bdy_nga)+
  tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +  
  tm_dots(col = "pct_handPump",
          border.col = "gray60",
          border.lwd = 0.5) +
  tm_view(set.zoom.limits = c(5,9))

mechPump <- tm_shape(bdy_nga)+
  tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +  
  tm_dots(col = "pct_mechPump",
          border.col = "gray60",
          border.lwd = 0.5) +
  tm_view(set.zoom.limits = c(5,9))

tapStand <- tm_shape(bdy_nga)+
  tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +  
  tm_dots(col = "pct_tapStand",
          border.col = "gray60",
          border.lwd = 0.5) +
  tm_view(set.zoom.limits = c(5,9))

tmap_arrange(handPump, mechPump, tapStand,
             asp=1, 
             ncol=2,
             sync = TRUE)
tmap_mode("plot")
tmap mode set to plotting

3.4.5.15 extract “usage_capacity” to wp_nga

freq(wp_joined1$usage_capacity, sort = "dec")
         n    % val%
300  68789 72.4 72.4
1000 25644 27.0 27.0
250    573  0.6  0.6
50       2  0.0  0.0

Remarks :

  • Only “300”, “1000”, and “250” are to be extracted for further analysis as the rest are either less than 0.5% or “Unknown”.

  • But, “50” will be included in the new variable “total_ucN1000” as part of the none ‘1000’ “usage_capacity” value.

uCap_300 <- wp_joined1 %>%
  filter(usage_capacity %in%
           "300")

uCap_1000 <- wp_joined1 %>%
  filter(usage_capacity %in%
           "1000")

uCap_250 <- wp_joined1 %>%
  filter(usage_capacity %in%
           "250")

uCap_50 <- wp_joined1 %>%
  filter(usage_capacity %in%
           "50")

wp_nga <- wp_nga %>%
  mutate(`total_uc300` = lengths(
    st_intersects(bdy_nga, uCap_300)
  )) %>%
  mutate(`total_uc1000` = lengths(
    st_intersects(bdy_nga, uCap_1000)
  )) %>%
  mutate(`total_uc250` = lengths(
    st_intersects(bdy_nga, uCap_250)
  )) %>%
  mutate(`total_uc50` = lengths(
    st_intersects(bdy_nga, uCap_50)
  )) %>%
  mutate(`total_ucN1000` = ((lengths(
    st_intersects(
      bdy_nga, uCap_300))) + (lengths(
        st_intersects(
          bdy_nga, uCap_250))) + (lengths(
            st_intersects(
              bdy_nga, uCap_50))))
    )%>%
           
  mutate(`pct_ucN1000` = (`total_ucN1000`/`total_wp`*100)) %>%
  mutate(`pct_uc300` = (`total_uc300`/`total_wp`*100)) %>%
  mutate(`pct_uc1000` = (`total_uc1000`/`total_wp`*100)) %>%
  mutate(`pct_uc250` = (`total_uc250`/`total_wp`*100))

-- replace “NaN” with 0

wp_nga <- wp_nga %>%
  mutate(`pct_ucN1000` = replace_na(pct_ucN1000, 0)) %>%
  mutate(`pct_uc300` = replace_na(pct_uc300, 0)) %>%
  mutate(`pct_uc1000` = replace_na(pct_uc1000, 0)) %>%
  mutate(`pct_uc250` = replace_na(pct_uc250, 0))

summary(wp_nga)
  shapeName                  geometry      total_wp     wp_functional   
 Length:774         MULTIPOLYGON :774   Min.   :  0.0   Min.   :  0.00  
 Class :character   epsg:4326    :  0   1st Qu.: 45.0   1st Qu.: 17.00  
 Mode  :character   +proj=long...:  0   Median : 96.0   Median : 45.50  
                                        Mean   :122.7   Mean   : 67.36  
                                        3rd Qu.:168.8   3rd Qu.: 87.75  
                                        Max.   :894.0   Max.   :752.00  
 pct_functional   wp_nonFunctional pct_nonFunctional   wp_unknown    
 Min.   :  0.00   Min.   :  0.00   Min.   :  0.00    Min.   :  0.00  
 1st Qu.: 32.61   1st Qu.: 12.00   1st Qu.: 20.77    1st Qu.:  0.00  
 Median : 47.41   Median : 33.50   Median : 34.89    Median :  0.00  
 Mean   : 49.84   Mean   : 41.37   Mean   : 35.58    Mean   : 13.76  
 3rd Qu.: 66.99   3rd Qu.: 60.00   3rd Qu.: 50.00    3rd Qu.: 17.75  
 Max.   :100.00   Max.   :278.00   Max.   :100.00    Max.   :219.00  
  pct_unknown     total_handPump   total_mechPump   total_tapStand   
 Min.   :  0.00   Min.   :  0.00   Min.   :  0.00   Min.   : 0.0000  
 1st Qu.:  0.00   1st Qu.:  6.00   1st Qu.: 11.00   1st Qu.: 0.0000  
 Median :  0.00   Median : 47.00   Median : 25.50   Median : 0.0000  
 Mean   : 12.55   Mean   : 75.89   Mean   : 33.12   Mean   : 0.7145  
 3rd Qu.: 20.83   3rd Qu.:111.00   3rd Qu.: 46.00   3rd Qu.: 0.0000  
 Max.   :100.00   Max.   :764.00   Max.   :245.00   Max.   :42.0000  
  pct_handPump     pct_mechPump     pct_tapStand      total_uc300    
 Min.   :  0.00   Min.   :  0.00   Min.   : 0.0000   Min.   :  0.00  
 1st Qu.: 16.70   1st Qu.: 12.20   1st Qu.: 0.0000   1st Qu.: 15.25  
 Median : 50.99   Median : 31.27   Median : 0.0000   Median : 59.00  
 Mean   : 48.73   Mean   : 37.54   Mean   : 0.5794   Mean   : 88.85  
 3rd Qu.: 77.78   3rd Qu.: 57.71   3rd Qu.: 0.0000   3rd Qu.:126.75  
 Max.   :100.00   Max.   :100.00   Max.   :32.8947   Max.   :767.00  
  total_uc1000     total_uc250        total_uc50       total_ucN1000   
 Min.   :  0.00   Min.   : 0.0000   Min.   :0.000000   Min.   :  0.00  
 1st Qu.: 11.00   1st Qu.: 0.0000   1st Qu.:0.000000   1st Qu.: 16.00  
 Median : 25.50   Median : 0.0000   Median :0.000000   Median : 60.00  
 Mean   : 33.12   Mean   : 0.7403   Mean   :0.002584   Mean   : 89.59  
 3rd Qu.: 46.00   3rd Qu.: 0.0000   3rd Qu.:0.000000   3rd Qu.:127.75  
 Max.   :245.00   Max.   :42.0000   Max.   :1.000000   Max.   :767.00  
  pct_ucN1000       pct_uc300        pct_uc1000       pct_uc250      
 Min.   :  0.00   Min.   :  0.00   Min.   :  0.00   Min.   : 0.0000  
 1st Qu.: 39.68   1st Qu.: 38.67   1st Qu.: 12.20   1st Qu.: 0.0000  
 Median : 67.03   Median : 65.91   Median : 31.27   Median : 0.0000  
 Mean   : 60.78   Mean   : 60.17   Mean   : 37.54   Mean   : 0.6114  
 3rd Qu.: 87.35   3rd Qu.: 87.02   3rd Qu.: 57.71   3rd Qu.: 0.0000  
 Max.   :100.00   Max.   :100.00   Max.   :100.00   Max.   :32.8947

3.4.5.16 visualise wp_nga distribution :: “usage_capacity”

tmap_mode("view")
tmap mode set to interactive viewing
uc300 <- tm_shape(bdy_nga)+
  tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +  
  tm_dots(col = "pct_uc300",
          border.col = "gray60",
          border.lwd = 0.5) +
  tm_view(set.zoom.limits = c(5,9))

uc1000 <- tm_shape(bdy_nga)+
  tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +  
  tm_dots(col = "pct_uc1000",
          border.col = "gray60",
          border.lwd = 0.5) +
  tm_view(set.zoom.limits = c(5,9))

uc250 <- tm_shape(bdy_nga)+
  tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +  
  tm_dots(col = "pct_uc250",
          border.col = "gray60",
          border.lwd = 0.5) +
  tm_view(set.zoom.limits = c(5,9))

tmap_arrange(uc300, uc1000, uc250,
             asp=1, 
             ncol=2,
             sync = TRUE)
tmap_mode("plot")
tmap mode set to plotting

3.4.5.17 extract “is_urban” to wp_nga

urban_1 <- wp_joined1 %>%
  filter(is_urban %in%
           "TRUE")

urban_0 <- wp_joined1 %>%
  filter(is_urban %in%
           "FALSE")

wp_nga <- wp_nga %>%
  mutate(`total_urban1` = lengths(
    st_intersects(bdy_nga, urban_1)
  )) %>%
  mutate(`total_urban0` = lengths(
    st_intersects(bdy_nga, urban_0)
  )) %>%
  mutate(`pct_urban1` = (`total_urban1`/`total_wp`*100)) %>%
  mutate(`pct_urban0` = (`total_urban0`/`total_wp`*100))

-- replace “NaN” with 0

wp_nga <- wp_nga %>%
  mutate(`pct_urban1` = replace_na(pct_urban1, 0)) %>%
  mutate(`pct_urban0` = replace_na(pct_urban0, 0))

summary(wp_nga)
  shapeName                  geometry      total_wp     wp_functional   
 Length:774         MULTIPOLYGON :774   Min.   :  0.0   Min.   :  0.00  
 Class :character   epsg:4326    :  0   1st Qu.: 45.0   1st Qu.: 17.00  
 Mode  :character   +proj=long...:  0   Median : 96.0   Median : 45.50  
                                        Mean   :122.7   Mean   : 67.36  
                                        3rd Qu.:168.8   3rd Qu.: 87.75  
                                        Max.   :894.0   Max.   :752.00  
 pct_functional   wp_nonFunctional pct_nonFunctional   wp_unknown    
 Min.   :  0.00   Min.   :  0.00   Min.   :  0.00    Min.   :  0.00  
 1st Qu.: 32.61   1st Qu.: 12.00   1st Qu.: 20.77    1st Qu.:  0.00  
 Median : 47.41   Median : 33.50   Median : 34.89    Median :  0.00  
 Mean   : 49.84   Mean   : 41.37   Mean   : 35.58    Mean   : 13.76  
 3rd Qu.: 66.99   3rd Qu.: 60.00   3rd Qu.: 50.00    3rd Qu.: 17.75  
 Max.   :100.00   Max.   :278.00   Max.   :100.00    Max.   :219.00  
  pct_unknown     total_handPump   total_mechPump   total_tapStand   
 Min.   :  0.00   Min.   :  0.00   Min.   :  0.00   Min.   : 0.0000  
 1st Qu.:  0.00   1st Qu.:  6.00   1st Qu.: 11.00   1st Qu.: 0.0000  
 Median :  0.00   Median : 47.00   Median : 25.50   Median : 0.0000  
 Mean   : 12.55   Mean   : 75.89   Mean   : 33.12   Mean   : 0.7145  
 3rd Qu.: 20.83   3rd Qu.:111.00   3rd Qu.: 46.00   3rd Qu.: 0.0000  
 Max.   :100.00   Max.   :764.00   Max.   :245.00   Max.   :42.0000  
  pct_handPump     pct_mechPump     pct_tapStand      total_uc300    
 Min.   :  0.00   Min.   :  0.00   Min.   : 0.0000   Min.   :  0.00  
 1st Qu.: 16.70   1st Qu.: 12.20   1st Qu.: 0.0000   1st Qu.: 15.25  
 Median : 50.99   Median : 31.27   Median : 0.0000   Median : 59.00  
 Mean   : 48.73   Mean   : 37.54   Mean   : 0.5794   Mean   : 88.85  
 3rd Qu.: 77.78   3rd Qu.: 57.71   3rd Qu.: 0.0000   3rd Qu.:126.75  
 Max.   :100.00   Max.   :100.00   Max.   :32.8947   Max.   :767.00  
  total_uc1000     total_uc250        total_uc50       total_ucN1000   
 Min.   :  0.00   Min.   : 0.0000   Min.   :0.000000   Min.   :  0.00  
 1st Qu.: 11.00   1st Qu.: 0.0000   1st Qu.:0.000000   1st Qu.: 16.00  
 Median : 25.50   Median : 0.0000   Median :0.000000   Median : 60.00  
 Mean   : 33.12   Mean   : 0.7403   Mean   :0.002584   Mean   : 89.59  
 3rd Qu.: 46.00   3rd Qu.: 0.0000   3rd Qu.:0.000000   3rd Qu.:127.75  
 Max.   :245.00   Max.   :42.0000   Max.   :1.000000   Max.   :767.00  
  pct_ucN1000       pct_uc300        pct_uc1000       pct_uc250      
 Min.   :  0.00   Min.   :  0.00   Min.   :  0.00   Min.   : 0.0000  
 1st Qu.: 39.68   1st Qu.: 38.67   1st Qu.: 12.20   1st Qu.: 0.0000  
 Median : 67.03   Median : 65.91   Median : 31.27   Median : 0.0000  
 Mean   : 60.78   Mean   : 60.17   Mean   : 37.54   Mean   : 0.6114  
 3rd Qu.: 87.35   3rd Qu.: 87.02   3rd Qu.: 57.71   3rd Qu.: 0.0000  
 Max.   :100.00   Max.   :100.00   Max.   :100.00   Max.   :32.8947  
  total_urban1     total_urban0      pct_urban1       pct_urban0    
 Min.   :  0.00   Min.   :  0.00   Min.   :  0.00   Min.   :  0.00  
 1st Qu.:  0.00   1st Qu.: 23.00   1st Qu.:  0.00   1st Qu.: 57.27  
 Median :  9.00   Median : 64.00   Median : 11.95   Median : 86.45  
 Mean   : 25.27   Mean   : 97.45   Mean   : 25.61   Mean   : 72.71  
 3rd Qu.: 33.00   3rd Qu.:141.00   3rd Qu.: 38.44   3rd Qu.:100.00  
 Max.   :324.00   Max.   :894.00   Max.   :100.00   Max.   :100.00

3.4.5.18 visualise wp_nga distribution :: “is_urban”

tmap_mode("view")
tmap mode set to interactive viewing
urban1 <- tm_shape(bdy_nga)+
  tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +  
  tm_dots(col = "pct_urban1",
          border.col = "gray60",
          border.lwd = 0.5) +
  tm_view(set.zoom.limits = c(5,9))

urban0 <- tm_shape(bdy_nga)+
  tm_polygons(alpha = 0.1) +
tm_shape(wp_nga) +  
  tm_dots(col = "pct_urban0",
          border.col = "gray60",
          border.lwd = 0.5) +
  tm_view(set.zoom.limits = c(5,9))

tmap_arrange(urban1, urban0,
             asp=1, 
             ncol=2,
             sync = TRUE)
tmap_mode("plot")
tmap mode set to plotting


3.4.5.19 save and read RDS File :: wp_nga

write_rds(wp_nga,"/jephOstan/ISSS624/class_project/project_2/data/geodata/wp_nga.rds")
wp_nga <- read_rds("/jephOstan/ISSS624/class_project/project_2/data/geodata/wp_nga.rds")

3.4.5.20 transform to Projected Coordinate System

Usage of the code chunk below :

st_crs( ) - sf - to inspect the coordinate reference system.

st_crs(wp_nga)
Coordinate Reference System:
  User input: EPSG:4326 
  wkt:
GEOGCRS["WGS 84",
    ENSEMBLE["World Geodetic System 1984 ensemble",
        MEMBER["World Geodetic System 1984 (Transit)"],
        MEMBER["World Geodetic System 1984 (G730)"],
        MEMBER["World Geodetic System 1984 (G873)"],
        MEMBER["World Geodetic System 1984 (G1150)"],
        MEMBER["World Geodetic System 1984 (G1674)"],
        MEMBER["World Geodetic System 1984 (G1762)"],
        MEMBER["World Geodetic System 1984 (G2139)"],
        ELLIPSOID["WGS 84",6378137,298.257223563,
            LENGTHUNIT["metre",1]],
        ENSEMBLEACCURACY[2.0]],
    PRIMEM["Greenwich",0,
        ANGLEUNIT["degree",0.0174532925199433]],
    CS[ellipsoidal,2],
        AXIS["geodetic latitude (Lat)",north,
            ORDER[1],
            ANGLEUNIT["degree",0.0174532925199433]],
        AXIS["geodetic longitude (Lon)",east,
            ORDER[2],
            ANGLEUNIT["degree",0.0174532925199433]],
    USAGE[
        SCOPE["Horizontal component of 3D system."],
        AREA["World."],
        BBOX[-90,-180,90,180]],
    ID["EPSG",4326]]

Remarks :

The EPSG for wp_nga is 4326, which is WGS 84. To compute the proximity distance matrix for clustering analysis, this coordinate reference system needs to transform into EPSG: 26391.

Usage of the code chunk below :

st_set_crs( ) - sf - to update the coordinate reference system.

wp_ngaTrans <- st_set_crs(wp_nga, 26391)
Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
that
bdy_ngaTrans <- st_set_crs(bdy_nga, 26391)
Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
that

-- review CRS :: wp_ngaTrans

st_crs(wp_ngaTrans)
Coordinate Reference System:
  User input: EPSG:26391 
  wkt:
PROJCRS["Minna / Nigeria West Belt",
    BASEGEOGCRS["Minna",
        DATUM["Minna",
            ELLIPSOID["Clarke 1880 (RGS)",6378249.145,293.465,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4263]],
    CONVERSION["Nigeria West Belt",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",4,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",4.5,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",0.99975,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",230738.26,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Engineering survey, topographic mapping."],
        AREA["Nigeria - onshore west of 6°30'E, onshore and offshore shelf."],
        BBOX[3.57,2.69,13.9,6.5]],
    ID["EPSG",26391]]

-- review CRS :: bdy_ngaTrans

st_crs(bdy_ngaTrans)
Coordinate Reference System:
  User input: EPSG:26391 
  wkt:
PROJCRS["Minna / Nigeria West Belt",
    BASEGEOGCRS["Minna",
        DATUM["Minna",
            ELLIPSOID["Clarke 1880 (RGS)",6378249.145,293.465,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4263]],
    CONVERSION["Nigeria West Belt",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",4,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",4.5,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",0.99975,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",230738.26,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Engineering survey, topographic mapping."],
        AREA["Nigeria - onshore west of 6°30'E, onshore and offshore shelf."],
        BBOX[3.57,2.69,13.9,6.5]],
    ID["EPSG",26391]]

3.5 Exploratory Data Analysis

3.5.1 Identify Outliers

3.5.1.1 plot boxplot “pct_functional”

ggplot(data=wp_ngaTrans, 
       aes(x=`pct_functional`)) +
  geom_boxplot(color="black", 
               fill="#543005")

3.5.1.2 plot boxplot “pct_nonFunctional”

ggplot(data=wp_ngaTrans, 
       aes(x=`pct_nonFunctional`)) +
  geom_boxplot(color="black", 
               fill="#C16622FF")

3.5.1.3 plot boxplot “pct_unknown”

ggplot(data=wp_ngaTrans, 
       aes(x=`pct_unknown`)) +
  geom_boxplot(color="black", 
               fill="#FFA319FF")

Remarks :

Among these 3 key categories of “status_clean”, “unknown” has the most outliers.

3.5.2 Multi-plot Histogram

3.5.2.1 plot histogram for “status_clean”

pctFunctional <- ggplot(data = wp_ngaTrans,
                         aes(x = `pct_functional`))+
  geom_histogram(bins=10,
                 colour = "black",
                 fill = "#543005")

pctNonFunctional <- ggplot(data = wp_ngaTrans,
                         aes(x = `pct_nonFunctional`))+
  geom_histogram(bins=10,
                 colour = "black",
                 fill = "#C16622FF")

pctUnknown <- ggplot(data = wp_ngaTrans,
                     aes(x = `pct_unknown`))+
  geom_histogram(bins = 10,
                 colour = "black",
                 fill = "#FFA319FF")
ggarrange(pctFunctional,pctNonFunctional,pctUnknown,
          ncol = 2,
          nrow = 2)

4. CORRELATION ANALYSIS

4.1 Create Data Table for Correlation Matrix Analysis

cluster_vars <- wp_ngaTrans %>%
  st_set_geometry(NULL) %>%
  select("shapeName",
         "pct_functional", 
         "pct_nonFunctional",
         "pct_unknown", 
         "pct_handPump",
         "pct_mechPump",
         "pct_tapStand",
         "pct_uc300",
         "pct_uc1000",
         "pct_ucN1000",
         "pct_uc250",
         "pct_urban0")
head(cluster_vars,5)
  shapeName pct_functional pct_nonFunctional pct_unknown pct_handPump
1 Aba North       41.17647          52.94118    5.882353    11.764706
2 Aba South       40.84507          46.47887    9.859155     9.859155
3    Abadam        0.00000           0.00000    0.000000     0.000000
4     Abaji       40.35088          59.64912    0.000000    40.350877
5      Abak       47.91667          50.00000    0.000000     8.333333
  pct_mechPump pct_tapStand pct_uc300 pct_uc1000 pct_ucN1000 pct_uc250
1     82.35294            0 17.647059   82.35294   17.647059         0
2     87.32394            0 12.676056   87.32394   12.676056         0
3      0.00000            0  0.000000    0.00000    0.000000         0
4     59.64912            0 40.350877   59.64912   40.350877         0
5     91.66667            0  8.333333   91.66667    8.333333         0
  pct_urban0
1   0.000000
2   5.633803
3   0.000000
4  84.210526
5  83.333333

4.2 Visualise Correlation Matrix

This plot allows to identify the pattern and the relationship in the matrix.

corrplot.mixed((cor(cluster_vars[,2:12])),
               upper = "number",
               lower = "ellipse",
               tl.col = "black",
               diag = "l",
               tl.pos = "lt")

Remarks :

Following are the pairs with strong correlation :

correlation coefficients variable_1 variable_2
1.00 pct_mechPump pct_uc1000
0.99 pct_tapStand pct_uc250
0.99 pct_uc300 pct_ucN1000
-0.91 pct_mechPump pct_ucN1000
-0.91 pct_uc1000 pct_ucN1000
-0.90 pct_mechPump pct_uc300
-0.90 pct_uc300 pct_uc1000

4.2.1 Replace Row ID with “shapeName”

row.names(cluster_vars) <- cluster_vars$shapeName
cluster_vars
                            shapeName pct_functional pct_nonFunctional
Aba North                   Aba North      41.176471        52.9411765
Aba South                   Aba South      40.845070        46.4788732
Abadam                         Abadam       0.000000         0.0000000
Abaji                           Abaji      40.350877        59.6491228
Abak                             Abak      47.916667        50.0000000
Abakaliki                   Abakaliki      35.193133        18.0257511
Abeokuta North         Abeokuta North      47.058824        44.1176471
Abeokuta South         Abeokuta South      60.504202        27.7310924
Abi                               Abi      51.973684        39.4736842
Aboh-Mbaise               Aboh-Mbaise      27.272727        37.8787879
Abua/Odual                 Abua/Odual      64.102564        33.3333333
Abuja Municipal       Abuja Municipal      40.000000        54.0740741
Adavi                           Adavi      44.444444        55.5555556
Ado                               Ado      42.968750        27.3437500
Ado-Odo/Ota               Ado-Odo/Ota      32.758621        41.9540230
Ado Ekiti                   Ado Ekiti      46.153846        43.1952663
Afijio                         Afijio      33.962264        34.9056604
Afikpo North             Afikpo North      43.010753        41.3978495
Afikpo South             Afikpo South      12.500000        50.0000000
Agaie                           Agaie      50.537634        49.4623656
Agatu                           Agatu      30.864198        66.6666667
Agege                           Agege      86.666667         6.6666667
Aguata                         Aguata      10.526316        15.7894737
Agwara                         Agwara      56.637168        41.5929204
Ahiazu-Mbaise           Ahiazu-Mbaise      26.666667        42.6666667
Ahoada East               Ahoada East      47.368421        52.6315789
Ahoada West               Ahoada West     100.000000         0.0000000
Aiyedade                     Aiyedade      40.712468        39.4402036
Aiyedire                     Aiyedire      50.857143        32.5714286
Aiyekire (Gbonyin) Aiyekire (Gbonyin)      44.186047        34.8837209
Ajaokuta                     Ajaokuta      40.000000        60.0000000
Ajeromi-Ifelodun     Ajeromi-Ifelodun      50.000000        12.5000000
Ajingi                         Ajingi      81.683168        18.3168317
Akamkpa                       Akamkpa      26.400000        37.6000000
Akinyele                     Akinyele      41.899441        34.0782123
Akko                             Akko      42.532468        56.4935065
Akoko-Edo                   Akoko-Edo      46.774194        50.0000000
Akoko North East     Akoko North East      47.136564        52.4229075
Akoko North West     Akoko North West      38.541667        59.3750000
Akoko South East     Akoko South East      32.051282        67.9487179
Akoko South West     Akoko South West      45.637584        54.3624161
Akpabuyo                     Akpabuyo      16.796875        52.7343750
Akuku Toru                 Akuku Toru      57.142857        28.5714286
Akure North               Akure North      42.553191        57.4468085
Akure South               Akure South      86.666667        13.3333333
Akwanga                       Akwanga      75.555556        23.3333333
Albasu                         Albasu      69.863014        30.1369863
Aleiro                         Aleiro      57.142857        41.9047619
Alimosho                     Alimosho      54.629630        22.5308642
Alkaleri                     Alkaleri      70.833333        29.1666667
Amuwo-Odofin             Amuwo-Odofin      50.000000        20.0000000
Anambra East             Anambra East      24.637681        23.1884058
Anambra West             Anambra West      48.148148        31.4814815
Anaocha                       Anaocha      32.876712        19.1780822
Andoni                         Andoni      35.294118        64.7058824
Aninri                         Aninri      40.000000         0.0000000
Aniocha North           Aniocha North      16.666667        66.6666667
Aniocha South           Aniocha South      53.846154        23.0769231
Anka                             Anka      76.000000        24.0000000
Ankpa                           Ankpa      20.370370        74.0740741
Apa                               Apa      30.769231        61.5384615
Apapa                           Apapa       0.000000         0.0000000
Ardo-Kola                   Ardo-Kola      51.658768        31.7535545
Arewa-Dandi               Arewa-Dandi      64.000000        36.0000000
Argungu                       Argungu      53.846154        46.1538462
Arochukwu                   Arochukwu      28.000000        28.0000000
Asa                               Asa      62.676056        29.5774648
Asari-Toru                 Asari-Toru      85.106383        10.6382979
Askira/Uba                 Askira/Uba      80.555556        19.4444444
Atakumosa East         Atakumosa East      43.946188        41.2556054
Atakumosa West         Atakumosa West      45.121951        41.8699187
Atiba                           Atiba      41.964286        25.8928571
Atigbo                         Atigbo      32.142857        45.5357143
Augie                           Augie      55.223881        44.7761194
Auyo                             Auyo      89.928058        10.0719424
Awe                               Awe      53.763441        43.0107527
Awgu                             Awgu      42.307692        30.7692308
Awka North                 Awka North      22.857143         0.0000000
Awka South                 Awka South      22.500000        22.5000000
Ayamelum                     Ayamelum      18.181818        57.5757576
Babura                         Babura      84.116331        15.8836689
Badagry                       Badagry      33.809524        48.0952381
Bade                             Bade      80.379747        19.6202532
Bagudo                         Bagudo      66.990291        33.0097087
Bagwai                         Bagwai      82.857143        17.1428571
Bakassi                       Bakassi       0.000000         0.0000000
Bakori                         Bakori      91.690544         8.3094556
Bakura                         Bakura      91.025641         7.6923077
Balanga                       Balanga      55.849057        44.1509434
Bali                             Bali      58.362989        29.1814947
Bama                             Bama       0.000000         0.0000000
Barikin Ladi             Barikin Ladi      27.177700        34.8432056
Baruten                       Baruten      66.871166        28.2208589
Bassa Kogi                 Bassa Kogi      41.269841        58.7301587
Bassa Plateau           Bassa Plateau      33.668342        25.1256281
Batagarawa                 Batagarawa      61.068702        38.9312977
Batsari                       Batsari      67.500000        32.5000000
Bauchi                         Bauchi      79.354839        20.6451613
Baure                           Baure      61.279461        38.7205387
Bayo                             Bayo      70.833333        29.1666667
Bebeji                         Bebeji      89.024390        10.9756098
Bekwara                       Bekwara      24.607330        40.3141361
Bende                           Bende      25.000000        25.0000000
Biase                           Biase      22.137405        44.2748092
Bichi                           Bichi      69.230769        30.7692308
Bida                             Bida      87.551867        12.4481328
Billiri                       Billiri      46.560847        52.3809524
Bindawa                       Bindawa      80.442804        19.5571956
Binji                           Binji      37.113402        62.8865979
Biriniwa                     Biriniwa      83.118557        16.8814433
Birni Kudu                 Birni Kudu      89.308176        10.6918239
Birnin-Gwari             Birnin-Gwari      29.940120        70.0598802
Birnin Kebbi             Birnin Kebbi      85.000000        13.3333333
Birnin Magaji           Birnin Magaji      78.350515        21.6494845
Biu                               Biu     100.000000         0.0000000
Bodinga                       Bodinga      52.040816        47.9591837
Bogoro                         Bogoro      78.225806        20.9677419
Boki                             Boki      40.223464        35.7541899
Bokkos                         Bokkos      33.333333        25.0000000
Boluwaduro                 Boluwaduro      48.837209        39.5348837
Bomadi                         Bomadi      25.000000        75.0000000
Bonny                           Bonny     100.000000         0.0000000
Borgu                           Borgu      74.074074        23.1481481
Boripe                         Boripe      44.632768        46.8926554
Bosso                           Bosso      73.684211        21.8045113
Brass                           Brass      27.272727        68.1818182
Buji                             Buji      87.537994        12.4620061
Bukkuyum                     Bukkuyum      72.222222        27.7777778
Bungudu                       Bungudu      68.571429        31.4285714
Bunkure                       Bunkure      76.377953        23.6220472
Bunza                           Bunza      42.331288        57.0552147
Bursari                       Bursari      81.443299        18.5567010
Buruku                         Buruku      39.204545        39.2045455
Burutu                         Burutu      42.857143        28.5714286
Bwari                           Bwari      49.152542        49.1525424
Calabar-Municipal   Calabar-Municipal      20.731707        31.7073171
Calabar South           Calabar South      26.027397        64.3835616
Chanchaga                   Chanchaga      44.444444        55.5555556
Charanchi                   Charanchi      75.126904        24.8730964
Chibok                         Chibok      75.757576        24.2424242
Chikun                         Chikun       0.000000       100.0000000
Dala                             Dala      61.594203        38.4057971
Damaturu                     Damaturu      92.307692         7.6923077
Damban                         Damban      37.837838        62.1621622
Dambatta                     Dambatta      64.532020        35.4679803
Damboa                         Damboa       0.000000         0.0000000
Dan Musa                     Dan Musa      89.062500        10.9375000
Dandi                           Dandi      29.696970        70.3030303
Dandume                       Dandume      62.608696        37.3913043
Dange-Shuni               Dange-Shuni      61.038961        38.9610390
Danja                           Danja      55.555556        44.4444444
Darazo                         Darazo      50.000000        50.0000000
Dass                             Dass      92.642140         7.3578595
Daura                           Daura      25.263158        74.7368421
Dawakin Kudu             Dawakin Kudu      70.142180        29.3838863
Dawakin Tofa             Dawakin Tofa      94.405594         5.5944056
Degema                         Degema      62.500000        25.0000000
Dekina                         Dekina      31.730769        67.3076923
Demsa                           Demsa      76.923077        23.0769231
Dikwa                           Dikwa       0.000000         0.0000000
Doguwa                         Doguwa      59.090909        40.9090909
Doma                             Doma      60.227273        38.6363636
Donga                           Donga      42.702703        44.8648649
Dukku                           Dukku      14.705882        85.2941176
Dunukofia                   Dunukofia      53.333333        35.5555556
Dutse                           Dutse      98.701299         1.2987013
Dutsi                           Dutsi      66.250000        33.7500000
Dutsin-Ma                   Dutsin-Ma      78.317152        21.6828479
Eastern Obolo           Eastern Obolo      16.000000        80.0000000
Ebonyi                         Ebonyi      26.923077        15.3846154
Edati                           Edati      54.787234        44.6808511
Ede North                   Ede North      65.277778        22.6851852
Ede South                   Ede South      49.315068        26.7123288
Edu                               Edu      49.218750        50.7812500
Efon                             Efon      20.000000        51.1111111
Egbado North             Egbado North      20.000000        38.3333333
Egbado South             Egbado South      81.250000         6.2500000
Egbeda                         Egbeda      32.989691        35.0515464
Egbedore                     Egbedore      45.283019        30.1886792
Egor                             Egor      48.076923        51.9230769
Ehime-Mbano               Ehime-Mbano      46.808511        48.9361702
Ejigbo                         Ejigbo      64.403292        26.3374486
Ekeremor                     Ekeremor      12.121212        87.8787879
Eket                             Eket      54.411765        45.5882353
Ekiti                           Ekiti      40.099010        59.9009901
Ekiti East                 Ekiti East      64.444444         4.4444444
Ekiti South West     Ekiti South West      40.740741        48.1481481
Ekiti West                 Ekiti West      48.717949        35.0427350
Ekwusigo                     Ekwusigo       5.555556         8.3333333
Eleme                           Eleme     100.000000         0.0000000
Emohua                         Emohua      66.666667        33.3333333
Emure                           Emure      44.615385        27.6923077
Enugu East                 Enugu East      60.869565        17.3913043
Enugu North               Enugu North      83.333333         4.1666667
Enugu South               Enugu South      42.105263         5.2631579
Epe                               Epe      43.478261        41.0628019
Esan Central             Esan Central      21.875000        62.5000000
Esan North East       Esan North East      41.176471        58.8235294
Esan South East       Esan South East      28.571429        71.4285714
Esan West                   Esan West      47.058824        52.9411765
Ese-Odo                       Ese-Odo      36.036036        63.0630631
Esit - Eket               Esit - Eket      25.000000        73.6842105
Essien Udim               Essien Udim      32.758621        63.7931034
Etche                           Etche      21.052632        47.3684211
Ethiope East             Ethiope East      36.363636        45.4545455
Ethiope West             Ethiope West      32.000000        64.0000000
Eti-Osa                       Eti-Osa      78.947368         0.0000000
Etim Ekpo                   Etim Ekpo      35.294118        64.7058824
Etinan                         Etinan      22.857143        77.1428571
Etsako Central         Etsako Central      35.416667        60.4166667
Etsako East               Etsako East      42.857143        54.7619048
Etsako West               Etsako West      66.666667        33.3333333
Etung                           Etung      27.878788        49.0909091
Ewekoro                       Ewekoro      26.388889        18.0555556
Ezeagu                         Ezeagu      15.686275        19.6078431
Ezinihitte                 Ezinihitte      26.785714        33.9285714
Ezza North                 Ezza North      39.010989        19.7802198
Ezza South                 Ezza South      42.993631        19.1082803
Fagge                           Fagge      93.650794         6.3492063
Fakai                           Fakai      81.818182        18.1818182
Faskari                       Faskari      77.101449        22.8985507
Fika                             Fika      78.947368        21.0526316
Fufore                         Fufore     100.000000         0.0000000
Funakaye                     Funakaye      41.578947        58.4210526
Fune                             Fune      73.239437        26.7605634
Funtua                         Funtua      75.714286        24.2857143
Gabasawa                     Gabasawa      97.385621         2.6143791
Gada                             Gada      64.406780        35.5932203
Gagarawa                     Gagarawa      92.584270         7.4157303
Gamawa                         Gamawa      66.817156        33.1828442
Ganjuwa                       Ganjuwa      86.419753        13.5802469
Ganye                           Ganye      64.285714        35.7142857
Garki                           Garki      79.907621        20.0923788
Garko                           Garko      87.640449        12.3595506
Garum Mallam             Garum Mallam      73.619632        26.3803681
Gashaka                       Gashaka      47.272727        38.7878788
Gassol                         Gassol      39.245283        39.2452830
Gaya                             Gaya      98.453608         1.5463918
Gbako                           Gbako      57.714286        42.2857143
Gboko                           Gboko      38.659794        37.1134021
Geidam                         Geidam       0.000000         0.0000000
Gezawa                         Gezawa      67.491166        32.5088339
Giade                           Giade      70.909091        28.1818182
Girei                           Girei      86.666667        13.3333333
Giwa                             Giwa      46.511628        53.4883721
Gokana                         Gokana      50.000000        50.0000000
Gombe                           Gombe      44.285714        54.2857143
Gombi                           Gombi      64.444444        33.3333333
Goronyo                       Goronyo      35.714286        64.2857143
Gubio                           Gubio       0.000000         0.0000000
Gudu                             Gudu      42.500000        57.5000000
Gujba                           Gujba       0.000000         0.0000000
Gulani                         Gulani      50.000000        50.0000000
Guma                             Guma      27.350427        52.1367521
Gumel                           Gumel      62.140992        37.8590078
Gummi                           Gummi      68.656716        31.3432836
Gurara                         Gurara      46.341463        28.0487805
Guri                             Guri      94.460641         5.5393586
Gusau                           Gusau      65.625000        34.3750000
Guyuk                           Guyuk      50.000000        45.4545455
Guzamala                     Guzamala       0.000000         0.0000000
Gwadabawa                   Gwadabawa      55.263158        44.7368421
Gwagwalada                 Gwagwalada      55.084746        43.2203390
Gwale                           Gwale      81.451613        18.5483871
Gwandu                         Gwandu      53.061224        46.9387755
Gwaram                         Gwaram      88.461538        11.5384615
Gwarzo                         Gwarzo      88.157895        11.8421053
Gwer East                   Gwer East      34.126984        60.3174603
Gwer West                   Gwer West       9.523810        85.7142857
Gwiwa                           Gwiwa      69.306931        30.6930693
Gwoza                           Gwoza       0.000000         0.0000000
Hadejia                       Hadejia      56.250000        43.7500000
Hawul                           Hawul      93.750000         6.2500000
Hong                             Hong      75.000000        25.0000000
Ibadan North             Ibadan North      54.545455        12.1212121
Ibadan North East   Ibadan North East      57.480315        35.4330709
Ibadan North West   Ibadan North West      66.666667        28.7356322
Ibadan South East   Ibadan South East      61.111111        16.6666667
Ibadan South West   Ibadan South West      68.224299        23.3644860
Ibaji                           Ibaji      20.588235        79.4117647
Ibarapa Central       Ibarapa Central      45.933014        33.4928230
Ibarapa East             Ibarapa East      37.254902        34.3137255
Ibarapa North           Ibarapa North      44.949495        33.3333333
Ibeju/Lekki               Ibeju/Lekki      48.872180        32.3308271
Ibeno                           Ibeno      40.000000        60.0000000
Ibesikpo Asutan       Ibesikpo Asutan      46.296296        51.8518519
Ibi                               Ibi      57.317073        29.2682927
Ibiono Ibom               Ibiono Ibom      39.215686        60.7843137
Idah                             Idah      37.288136        61.0169492
Idanre                         Idanre      46.153846        52.8846154
Ideato North             Ideato North      21.428571        14.2857143
Ideato South             Ideato South      30.434783        26.0869565
Idemili North           Idemili North      10.204082         4.0816327
Idemili South           Idemili South      12.500000         7.1428571
Ido                               Ido      52.427184        28.1553398
Ido-Osi                       Ido-Osi      39.583333         6.2500000
Ifako-Ijaye               Ifako-Ijaye      52.459016        31.1475410
Ife Central               Ife Central      37.662338        59.7402597
Ife East                     Ife East      39.593909        51.7766497
Ife North                   Ife North      48.905109        43.7956204
Ife South                   Ife South      48.876404        41.0112360
Ifedayo                       Ifedayo      35.714286        41.6666667
Ifedore                       Ifedore      40.000000        60.0000000
Ifelodun Kwara         Ifelodun Kwara      53.244592        46.2562396
Ifelodun Osun           Ifelodun Osun      33.333333        60.3174603
Ifo                               Ifo       9.638554        12.0481928
Igabi                           Igabi      26.279863        73.7201365
Igalamela-Odolu       Igalamela-Odolu      41.666667        54.1666667
Igbo-Etiti                 Igbo-Etiti      27.272727        27.2727273
Igbo-Eze North         Igbo-Eze North      47.619048         7.1428571
Igbo-Eze South         Igbo-Eze South      38.235294         8.8235294
Igueben                       Igueben      20.000000        80.0000000
Ihiala                         Ihiala      21.374046        22.1374046
Ihitte/Uboma             Ihitte/Uboma      25.000000        47.2222222
Ijebu East                 Ijebu East      16.129032         6.4516129
Ijebu North               Ijebu North      21.875000        53.1250000
Ijebu North East     Ijebu North East      30.000000        45.5555556
Ijebu Ode                   Ijebu Ode      51.724138        27.5862069
Ijero                           Ijero      31.553398        47.5728155
Ijumu                           Ijumu      49.180328        50.8196721
Ika                               Ika      16.326531        83.6734694
Ika North East         Ika North East     100.000000         0.0000000
Ika South                   Ika South      26.666667        73.3333333
Ikara                           Ikara      87.591241        12.4087591
Ikeduru                       Ikeduru      13.333333        33.3333333
Ikeja                           Ikeja      61.904762         4.7619048
Ikenne                         Ikenne      26.315789        21.0526316
Ikere                           Ikere      34.020619        55.6701031
Ikole                           Ikole      33.513514        36.7567568
Ikom                             Ikom      30.845771        42.7860697
Ikono                           Ikono      41.269841        58.7301587
Ikorodu                       Ikorodu      54.583333        26.2500000
Ikot Abasi                 Ikot Abasi      43.103448        56.8965517
Ikot Ekpene               Ikot Ekpene      31.914894        68.0851064
Ikpoba-Okha               Ikpoba-Okha      43.939394        56.0606061
Ikwerre                       Ikwerre      93.939394         6.0606061
Ikwo                             Ikwo      21.698113        14.7798742
Ikwuano                       Ikwuano      12.149533        40.1869159
Ila                               Ila      43.930636        38.1502890
Ilaje                           Ilaje      48.275862        51.7241379
Ile-Oluji-Okeigbo   Ile-Oluji-Okeigbo      33.812950        65.4676259
Ilejemeji                   Ilejemeji      21.428571        54.7619048
Ilesha East               Ilesha East      43.165468        38.8489209
Ilesha West               Ilesha West      49.484536        46.3917526
Illela                         Illela      43.181818        56.8181818
Ilorin East               Ilorin East      68.468468        31.5315315
Ilorin South             Ilorin South      62.745098        36.2745098
Ilorin West               Ilorin West      62.745098        37.2549020
Imeko-Afon                 Imeko-Afon      12.121212         0.0000000
Ingawa                         Ingawa      89.393939        10.6060606
Ini                               Ini      39.130435        60.8695652
Ipokia                         Ipokia      34.920635        39.6825397
Irele                           Irele      34.482759        65.5172414
Irepo                           Irepo      42.372881        32.2033898
Irepodun Kwara         Irepodun Kwara      40.769231        58.4615385
Irepodun Osun           Irepodun Osun      47.115385        40.3846154
Irepodun/Ifelodun   Irepodun/Ifelodun      36.283186        26.5486726
Irewole                       Irewole      45.045045        45.0450450
Isa                               Isa      83.018868        16.9811321
Ise/Orun                     Ise/Orun      65.714286         2.8571429
Iseyin                         Iseyin      25.471698        11.3207547
Ishielu                       Ishielu      29.918033        34.4262295
Isi-Uzo                       Isi-Uzo       1.724138        74.1379310
Isiala-Ngwa North   Isiala-Ngwa North      23.595506        34.8314607
Isiala-Ngwa South   Isiala-Ngwa South      23.437500        51.5625000
Isiala Mbano             Isiala Mbano      42.105263        44.7368421
Isin                             Isin      60.645161        36.1290323
Isiukwuato                 Isiukwuato      39.344262        27.8688525
Isokan                         Isokan      40.650407        39.8373984
Isoko North               Isoko North      38.461538        61.5384615
Isoko South               Isoko South      43.750000        56.2500000
Isu                               Isu      29.166667         8.3333333
Itas/Gadau                 Itas/Gadau      72.027972        27.9720280
Itesiwaju                   Itesiwaju      56.338028         9.8591549
Itu                               Itu      23.333333        75.0000000
Ivo                               Ivo      15.909091        38.6363636
Iwajowa                       Iwajowa      47.450980        31.3725490
Iwo                               Iwo      53.125000        26.2500000
Izzi                             Izzi      22.257053         9.0909091
Jaba                             Jaba      39.215686        58.8235294
Jada                             Jada      57.142857        42.8571429
Jahun                           Jahun      85.984848        14.0151515
Jakusko                       Jakusko      88.571429        11.4285714
Jalingo                       Jalingo      47.101449        42.0289855
Jama'are                     Jama'are      70.634921        29.3650794
Jega                             Jega      52.027027        47.9729730
Jema'a                         Jema'a      46.757679        53.2423208
Jere                             Jere      50.000000        24.3421053
Jibia                           Jibia      66.315789        33.6842105
Jos East                     Jos East      31.840796        26.3681592
Jos North                   Jos North      51.538462        20.7692308
Jos South                   Jos South      23.529412        45.4545455
Kabba/Bunu                 Kabba/Bunu      47.945205        52.0547945
Kabo                             Kabo      81.250000        18.7500000
Kachia                         Kachia      61.057692        38.9423077
Kaduna North             Kaduna North      39.655172        60.3448276
Kaduna South             Kaduna South      95.270270         4.7297297
Kafin Hausa               Kafin Hausa      99.593496         0.4065041
Kafur                           Kafur      79.674797        20.3252033
Kaga                             Kaga       0.000000         0.0000000
Kagarko                       Kagarko      30.810811        69.1891892
Kaiama                         Kaiama      55.339806        39.8058252
Kaita                           Kaita      66.777409        33.2225914
Kajola                         Kajola      47.191011        30.8988764
Kajuru                         Kajuru      96.503497         3.1468531
Kala/Balge                 Kala/Balge       0.000000         0.0000000
Kalgo                           Kalgo      32.380952        67.6190476
Kaltungo                     Kaltungo      73.000000        26.6666667
Kanam                           Kanam      32.068966        25.8620690
Kankara                       Kankara      81.102362        18.8976378
Kanke                           Kanke      48.437500        19.8660714
Kankia                         Kankia      74.100719        25.8992806
Kano Municipal         Kano Municipal      77.155172        22.8448276
Karasuwa                     Karasuwa      79.411765        20.5882353
Karaye                         Karaye      57.522124        42.4778761
Karim-Lamido             Karim-Lamido      37.037037        44.4444444
Karu                             Karu      55.617978        36.5168539
Katagum                       Katagum      67.796610        32.2033898
Katcha                         Katcha      47.826087        34.7826087
Katsina                       Katsina      65.909091        34.0909091
Katsina-Ala               Katsina-Ala      32.911392        33.5443038
Kaugama                       Kaugama      94.373866         5.6261343
Kaura                           Kaura      58.156028        41.8439716
Kaura Namoda             Kaura Namoda      80.000000        20.0000000
Kauru                           Kauru      68.786127        30.6358382
Kazaure                       Kazaure      71.801567        28.1984334
Keana                           Keana      48.051948        51.9480519
Kebbe                           Kebbe      54.687500        45.3125000
Keffi                           Keffi      77.922078        20.7792208
Khana                           Khana      51.111111        42.2222222
Kibiya                         Kibiya      80.751174        19.2488263
Kirfi                           Kirfi      45.977011        43.6781609
Kiri Kasamma             Kiri Kasamma      79.959920        20.0400802
Kiru                             Kiru      76.211454        22.4669604
Kiyawa                         Kiyawa      83.333333        16.6666667
Kogi                             Kogi      40.000000        60.0000000
Koko/Besse                 Koko/Besse      33.108108        66.8918919
Kokona                         Kokona      57.142857        41.9047619
Kolokuma/Opokuma     Kolokuma/Opokuma      90.000000         5.0000000
Konduga                       Konduga      66.666667         0.0000000
Konshisha                   Konshisha      46.537396        24.0997230
Kontagora                   Kontagora      59.259259        38.2716049
Kosofe                         Kosofe      75.862069        10.3448276
Kubau                           Kubau      92.083333         7.9166667
Kudan                           Kudan      43.506494        56.4935065
Kuje                             Kuje      39.860140        51.7482517
Kukawa                         Kukawa       0.000000         0.0000000
Kumbotso                     Kumbotso      82.352941        17.6470588
Kunchi                         Kunchi      89.062500        10.9375000
Kura                             Kura      67.175573        32.8244275
Kurfi                           Kurfi      66.990291        33.0097087
Kurmi                           Kurmi      42.857143        41.8367347
Kusada                         Kusada      97.058824         2.9411765
Kwali                           Kwali      57.865169        41.0112360
Kwami                           Kwami      34.666667        65.3333333
Kwande                         Kwande      22.988506        64.3678161
Kware                           Kware      62.595420        36.6412214
Kwaya Kusar               Kwaya Kusar      66.666667        33.3333333
Lafia                           Lafia      61.254613        34.6863469
Lagelu                         Lagelu      42.400000        40.8000000
Lagos Island             Lagos Island      70.270270        29.7297297
Lagos Mainland         Lagos Mainland      69.047619         4.7619048
Lamurde                       Lamurde     100.000000         0.0000000
Langtang North         Langtang North      41.066667        24.8000000
Langtang South         Langtang South      19.565217        44.5652174
Lapai                           Lapai      76.271186        22.8813559
Lau                               Lau      42.045455        43.1818182
Lavun                           Lavun      65.000000        34.0000000
Lere                             Lere      60.326087        39.6739130
Logo                             Logo      27.210884        41.4965986
Lokoja                         Lokoja      71.929825        28.0701754
Machina                       Machina      81.081081        18.9189189
Madagali                     Madagali       0.000000         0.0000000
Madobi                         Madobi      86.857143        13.1428571
Mafa                             Mafa       0.000000         0.0000000
Magama                         Magama      51.000000        44.0000000
Magumeri                     Magumeri      77.777778        22.2222222
Mai'adua                     Mai'adua      55.776892        44.2231076
Maiduguri                   Maiduguri      65.413534         3.0075188
Maigatari                   Maigatari      75.294118        24.7058824
Maiha                           Maiha      76.470588        23.5294118
Maiyama                       Maiyama      46.000000        54.0000000
Makoda                         Makoda      98.113208         1.8867925
Makurdi                       Makurdi      56.521739        40.2173913
Malam Madori             Malam Madori      93.650794         6.3492063
Malumfashi                 Malumfashi      82.291667        17.7083333
Mangu                           Mangu      34.810127        31.3291139
Mani                             Mani      60.689655        39.3103448
Maradun                       Maradun      69.791667        30.2083333
Mariga                         Mariga      48.305085        50.8474576
Markafi                       Markafi      97.368421         1.7543860
Marte                           Marte       0.000000         0.0000000
Maru                             Maru      70.658683        29.3413174
Mashegu                       Mashegu      32.142857        64.2857143
Mashi                           Mashi      51.639344        48.3606557
Matazu                         Matazu      75.129534        24.8704663
Mayo-Belwa                 Mayo-Belwa      75.000000        25.0000000
Mbaitoli                     Mbaitoli      23.636364        23.6363636
Mbo                               Mbo      21.428571        78.5714286
Michika                       Michika      75.000000        25.0000000
Miga                             Miga      98.437500         1.5625000
Mikang                         Mikang      33.898305        31.5254237
Minjibir                     Minjibir      99.029126         0.9708738
Misau                           Misau      70.992366        29.0076336
Mkpat Enin                 Mkpat Enin      33.913043        66.0869565
Moba                             Moba      19.736842        42.1052632
Mobbar                         Mobbar       0.000000         0.0000000
Mokwa                           Mokwa      60.526316        39.4736842
Monguno                       Monguno       0.000000         0.0000000
Mopa-Muro                   Mopa-Muro      32.558140        67.4418605
Moro                             Moro      57.714286        37.1428571
Mubi North                 Mubi North       0.000000       100.0000000
Mubi South                 Mubi South       0.000000       100.0000000
Musawa                         Musawa      85.314685        14.6853147
Mushin                         Mushin      75.000000        15.6250000
Muya                             Muya      55.789474        44.2105263
Nafada                         Nafada      46.621622        53.3783784
Nangere                       Nangere      72.972973        27.0270270
Nasarawa Kano           Nasarawa Kano      51.000000        49.0000000
Nasarawa                     Nasarawa      48.275862        45.5172414
Nasarawa-Eggon         Nasarawa-Eggon      53.459119        44.6540881
Ndokwa East               Ndokwa East      44.578313        54.2168675
Ndokwa West               Ndokwa West      39.130435        56.5217391
Nembe                           Nembe      37.209302        62.7906977
Ngala                           Ngala       0.000000         0.0000000
Nganzai                       Nganzai       0.000000         0.0000000
Ngaski                         Ngaski      51.304348        48.6956522
Ngor-Okpala               Ngor-Okpala      33.823529        17.6470588
Nguru                           Nguru      91.216216         8.7837838
Ningi                           Ningi      81.521739        18.4782609
Njaba                           Njaba       6.666667        46.6666667
Njikoka                       Njikoka      32.352941        38.2352941
Nkanu East                 Nkanu East      37.037037         1.8518519
Nkanu West                 Nkanu West      21.518987        45.5696203
Nkwerre                       Nkwerre      42.424242        54.5454545
Nnewi North               Nnewi North      30.769231        17.9487179
Nnewi South               Nnewi South       3.030303         6.0606061
Nsit Atai                   Nsit Atai      23.529412        75.0000000
Nsit Ibom                   Nsit Ibom      46.938776        53.0612245
Nsit Ubium                 Nsit Ubium      43.181818        56.8181818
Nsukka                         Nsukka      19.642857        17.8571429
Numan                           Numan     100.000000         0.0000000
Nwangele                     Nwangele      14.285714        60.7142857
Obafemi-Owode           Obafemi-Owode      36.363636        22.7272727
Obanliku                     Obanliku      34.615385        36.2637363
Nasarawa Nasarawa   Nasarawa Nasarawa      49.397590        28.9156627
Obi Nasarawa             Obi Nasarawa      27.272727        72.7272727
Obi Ngwa                     Obi Ngwa      17.341040        64.7398844
Obia/Akpor                 Obia/Akpor      64.800000        34.4000000
Obokun                         Obokun      53.960396        41.0891089
Obot Akara                 Obot Akara      23.809524        63.4920635
Obowo                           Obowo      21.212121        37.8787879
Obubra                         Obubra      33.905579        49.3562232
Obudu                           Obudu      36.820084        38.4937238
Odeda                           Odeda      15.929204        32.7433628
Odigbo                         Odigbo      22.674419        77.3255814
Odo-Otin                     Odo-Otin      45.723684        38.8157895
Odogbolu                     Odogbolu      51.948052        28.5714286
Odukpani                     Odukpani      40.119760        54.4910180
Offa                             Offa      43.617021        56.3829787
Ofu                               Ofu      32.352941        67.6470588
Ogba/Egbema/Ndoni   Ogba/Egbema/Ndoni      56.451613        19.3548387
Ogbadibo                     Ogbadibo      37.037037        41.9753086
Ogbaru                         Ogbaru      43.478261        26.0869565
Ogbia                           Ogbia      52.500000        45.0000000
Ogbomosho North       Ogbomosho North      69.000000        28.0000000
Ogbomosho South       Ogbomosho South      49.137931        50.0000000
Ogo Oluwa                   Ogo Oluwa      54.301075        41.3978495
Ogoja                           Ogoja      29.304029        32.6007326
Ogori/Magongo           Ogori/Magongo      47.916667        52.0833333
Ogu/Bolo                     Ogu/Bolo     100.000000         0.0000000
Ogun waterside         Ogun waterside      56.756757        39.1891892
Oguta                           Oguta      16.161616        59.5959596
Ohafia                         Ohafia      21.052632        50.0000000
Ohaji/Egbema             Ohaji/Egbema      58.426966        28.6516854
Ohaozara                     Ohaozara      38.545455        25.8181818
Ohaukwu                       Ohaukwu      36.694678        23.5294118
Ohimini                       Ohimini      41.538462        58.4615385
Oji-River                   Oji-River      13.157895        34.2105263
Ojo                               Ojo      17.241379        36.7816092
Oju                               Oju      87.545788        10.9890110
Oke-Ero                       Oke-Ero      40.579710        58.4541063
Okehi                           Okehi      27.027027        72.9729730
Okene                           Okene      51.000000        49.0000000
Okigwe                         Okigwe      31.707317        58.5365854
Okitipupa                   Okitipupa      29.333333        70.6666667
Okobo                           Okobo      33.783784        63.5135135
Okpe                             Okpe      30.769231        38.4615385
Okpokwu                       Okpokwu      33.898305        52.5423729
Okrika                         Okrika      91.666667         8.3333333
Ola-oluwa                   Ola-oluwa      51.351351         9.4594595
Olamabolo                   Olamabolo      34.782609        65.2173913
Olorunda                     Olorunda      51.381215        36.4640884
Olorunsogo                 Olorunsogo      32.323232        31.3131313
Oluyole                       Oluyole      40.540541        22.9729730
Omala                           Omala      13.513514        86.4864865
Omumma                         Omumma      76.000000        24.0000000
Ona-Ara                       Ona-Ara      25.438596        51.7543860
Ondo East                   Ondo East      35.555556        64.4444444
Ondo West                   Ondo West      34.161491        65.2173913
Onicha                         Onicha      45.433790        22.6027397
Onitsha North           Onitsha North       7.692308         7.6923077
Onitsha South           Onitsha South      41.666667        16.6666667
Onna                             Onna      42.857143        57.1428571
Opobo/Nkoro               Opobo/Nkoro      54.545455        45.4545455
Oredo                           Oredo      56.521739        43.4782609
Orelope                       Orelope      24.489796        39.7959184
Orhionmwon                 Orhionmwon      39.639640        60.3603604
Ori Ire                       Ori Ire      42.677824        15.0627615
Oriade                         Oriade      43.243243        38.6100386
Orlu                             Orlu      37.254902        27.4509804
Orolu                           Orolu      36.666667        55.8333333
Oron                             Oron      60.000000        40.0000000
Orsu                             Orsu      45.000000        35.0000000
Oru East                     Oru East      15.384615        23.0769231
Oru West                     Oru West      19.512195        21.9512195
Oruk Anam                   Oruk Anam      16.949153        83.0508475
Orumba North             Orumba North      19.696970        21.2121212
Orumba South             Orumba South      18.181818        33.3333333
Ose                               Ose      30.000000        69.3333333
Oshimili North         Oshimili North      84.848485        15.1515152
Oshimili South         Oshimili South      47.826087        47.8260870
Oshodi-Isolo             Oshodi-Isolo      68.085106         0.0000000
Osisioma Ngwa           Osisioma Ngwa      15.909091        36.3636364
Osogbo                         Osogbo      52.906977        36.6279070
Oturkpo                       Oturkpo      32.258065        54.8387097
Ovia North East       Ovia North East      51.807229        39.7590361
Ovia South West       Ovia South West      54.545455        38.9610390
Owan East                   Owan East      52.380952        46.0317460
Owan West                   Owan West      22.727273        77.2727273
Owerri-Municipal     Owerri-Municipal      64.516129         6.4516129
Owerri North             Owerri North      30.882353        44.1176471
Owerri West               Owerri West      47.540984        32.7868852
Owo                               Owo      49.171271        49.1712707
Oye                               Oye      33.802817        50.7042254
Oyi                               Oyi      23.287671        36.9863014
Oyigbo                         Oyigbo      88.235294        11.7647059
Oyo East                     Oyo East      39.772727        34.0909091
Oyo West                     Oyo West      28.125000        18.7500000
Oyun                             Oyun      45.373134        54.6268657
Paikoro                       Paikoro      55.319149        43.6170213
Pankshin                     Pankshin      31.909548        25.1256281
Patani                         Patani      18.181818        81.8181818
Pategi                         Pategi      56.000000        40.7272727
Port-Harcourt           Port-Harcourt      88.695652        11.3043478
Potiskum                     Potiskum      91.666667         8.3333333
Qua'an Pan                 Qua'an Pan      31.594203        31.5942029
Rabah                           Rabah      92.063492         7.9365079
Rafi                             Rafi      68.939394        31.0606061
Rano                             Rano      77.000000        23.0000000
Remo North                 Remo North      27.272727        43.1818182
Rijau                           Rijau      42.384106        50.3311258
Rimi                             Rimi      60.439560        39.5604396
Rimin Gado                 Rimin Gado      96.987952         3.0120482
Ringim                         Ringim      96.527778         3.4722222
Riyom                           Riyom      25.000000        33.5526316
Rogo                             Rogo      61.621622        38.3783784
Roni                             Roni      65.333333        34.6666667
Sabon-Gari                 Sabon-Gari      55.303030        44.6969697
Sabon Birni               Sabon Birni      36.363636        63.6363636
Sabuwa                         Sabuwa      23.255814        76.7441860
Safana                         Safana      86.335404        13.6645963
Sagbama                       Sagbama      29.032258        66.1290323
Sakaba                         Sakaba      52.413793        47.5862069
Saki East                   Saki East      49.541284        36.6972477
Saki West                   Saki West      43.918919        33.1081081
Sandamu                       Sandamu      60.550459        39.4495413
Sanga                           Sanga      51.298701        48.7012987
Sapele                         Sapele      33.333333        58.3333333
Sardauna                     Sardauna      28.000000        56.0000000
Shagamu                       Shagamu      13.934426        50.8196721
Shagari                       Shagari      67.307692        30.7692308
Shanga                         Shanga      29.126214        70.8737864
Shani                           Shani     100.000000         0.0000000
Shanono                       Shanono      72.289157        27.7108434
Shelleng                     Shelleng      55.555556        44.4444444
Shendam                       Shendam      26.401869        25.4672897
Shinkafi                     Shinkafi      75.159236        24.8407643
Shira                           Shira      75.609756        24.3902439
Shiroro                       Shiroro      66.055046        22.9357798
Shomgom                       Shomgom      55.378486        44.6215139
Shomolu                       Shomolu      57.142857        21.4285714
Silame                         Silame      75.000000        25.0000000
Soba                             Soba      61.743341        38.2566586
Sokoto North             Sokoto North      90.909091         9.0909091
Sokoto South             Sokoto South      76.000000        20.0000000
Song                             Song      75.000000        25.0000000
Southern Ijaw           Southern Ijaw      22.580645        74.1935484
Sule-Tankarkar         Sule-Tankarkar      86.597938        13.4020619
Suleja                         Suleja      70.731707        29.2682927
Sumaila                       Sumaila      74.007220        25.9927798
Suru                             Suru      21.666667        78.3333333
Obi Benue                   Obi Benue      80.327869         9.8360656
Surulere Lagos         Surulere Lagos      48.148148        47.6190476
Tafa                             Tafa      87.500000        12.5000000
Tafawa-Balewa           Tafawa-Balewa      82.031250        16.4062500
Tai                               Tai      33.333333        66.6666667
Takai                           Takai      78.876404        21.1235955
Takum                           Takum      45.555556        46.6666667
Talata Mafara           Talata Mafara      63.101604        36.8983957
Tambuwal                     Tambuwal      44.943820        55.0561798
Tangaza                       Tangaza      35.937500        64.0625000
Tarauni                       Tarauni      91.044776         8.9552239
Tarka                           Tarka      57.589286        22.7678571
Tarmua                         Tarmua      86.111111        13.8888889
Taura                           Taura      88.872180        11.1278195
Tofa                             Tofa      99.099099         0.9009009
Toro                             Toro      75.939850        21.0526316
Toto                             Toto      58.974359        38.4615385
Toungo                         Toungo      80.000000        20.0000000
Tsafe                           Tsafe      77.889447        22.1105528
Tsanyawa                     Tsanyawa      72.903226        27.0967742
Tudun Wada                 Tudun Wada      55.621302        43.7869822
Tureta                         Tureta      39.639640        59.4594595
Udenu                           Udenu      22.727273         4.5454545
Udi                               Udi      22.857143         9.5238095
Udu                               Udu      63.636364        36.3636364
Udung Uko                   Udung Uko      22.222222        77.7777778
Ughelli North           Ughelli North      58.490566        33.9622642
Ughelli South           Ughelli South      44.000000        56.0000000
Ugwunagbo                   Ugwunagbo      14.772727        46.5909091
Uhunmwonde                 Uhunmwonde      52.380952        44.4444444
Ukanafun                     Ukanafun      19.047619        80.9523810
Ukum                             Ukum      27.215190        46.2025316
Ukwa East                   Ukwa East      24.444444        66.6666667
Ukwa West                   Ukwa West      26.612903        52.4193548
Ukwuani                       Ukwuani      25.925926        72.2222222
Umu-Nneochi               Umu-Nneochi      11.363636        29.5454545
Umuahia North           Umuahia North      41.891892        22.9729730
Umuahia South           Umuahia South      16.504854        39.8058252
Ungogo                         Ungogo      91.390728         8.6092715
Unuimo                         Unuimo      16.666667        33.3333333
Uruan                           Uruan      16.666667        83.3333333
Urue-Offong/Oruko   Urue-Offong/Oruko      28.571429        71.4285714
Ushongo                       Ushongo      27.510917        42.7947598
Ussa                             Ussa      47.027027        41.6216216
Uvwie                           Uvwie      27.272727        72.7272727
Uyo                               Uyo      43.750000        56.2500000
Uzo-Uwani                   Uzo-Uwani       8.000000         4.0000000
Vandeikya                   Vandeikya      34.523810        31.5476190
Wamako                         Wamako      53.086420        46.9135802
Wamba                           Wamba      55.882353        43.1372549
Warawa                         Warawa      72.864322        27.1356784
Warji                           Warji      87.272727        12.7272727
Warri North               Warri North      23.333333        73.3333333
Warri South               Warri South      48.076923        51.9230769
Warri South West     Warri South West      14.285714        85.7142857
Wasagu/Danko             Wasagu/Danko      62.285714        37.7142857
Wase                             Wase      37.640449        28.0898876
Wudil                           Wudil      74.305556        25.6944444
Wukari                         Wukari      41.975309        38.6831276
Wurno                           Wurno      62.711864        37.2881356
Wushishi                     Wushishi      51.798561        48.2014388
Yabo                             Yabo      37.804878        62.1951220
Yagba East                 Yagba East      28.813559        71.1864407
Yagba West                 Yagba West      34.453782        65.5462185
Yakurr                         Yakurr      51.692308        37.8461538
Yala                             Yala      25.958702        31.8584071
Yamaltu/Deba             Yamaltu/Deba      49.193548        50.8064516
Yankwashi                   Yankwashi      84.070796        15.9292035
Yauri                           Yauri      46.153846        53.8461538
Yenegoa                       Yenegoa      44.444444        55.5555556
Yola North                 Yola North      88.461538        11.5384615
Yola South                 Yola South      46.153846        53.8461538
Yorro                           Yorro      59.296482        24.6231156
Yunusari                     Yunusari     100.000000         0.0000000
Yusufari                     Yusufari      88.095238        11.9047619
Zaki                             Zaki      87.706856        12.2931442
Zango                           Zango      80.373832        19.6261682
Zango-Kataf               Zango-Kataf      95.115681         4.3701799
Zaria                           Zaria      52.664577        47.3354232
Zing                             Zing      55.263158        33.6842105
Zurmi                           Zurmi      65.891473        34.1085271
Zuru                             Zuru      61.428571        38.5714286
                   pct_unknown pct_handPump pct_mechPump pct_tapStand
Aba North            5.8823529   11.7647059   82.3529412    0.0000000
Aba South            9.8591549    9.8591549   87.3239437    0.0000000
Abadam               0.0000000    0.0000000    0.0000000    0.0000000
Abaji                0.0000000   40.3508772   59.6491228    0.0000000
Abak                 0.0000000    8.3333333   91.6666667    0.0000000
Abakaliki           46.7811159   43.7768240    9.4420601   15.4506438
Abeokuta North       8.8235294   14.7058824   76.4705882    0.0000000
Abeokuta South      11.7647059   16.8067227   70.5882353    0.0000000
Abi                  7.2368421   59.8684211   32.8947368    0.0000000
Aboh-Mbaise         33.3333333    1.5151515   65.1515152    0.0000000
Abua/Odual           2.5641026   30.7692308   66.6666667    0.0000000
Abuja Municipal      5.9259259   34.0740741   59.2592593    0.0000000
Adavi                0.0000000   46.0317460   53.9682540    0.0000000
Ado                 28.9062500   67.1875000    3.1250000    0.7812500
Ado-Odo/Ota         25.2873563    9.1954023   65.5172414    0.0000000
Ado Ekiti           10.6508876   53.8461538   33.1360947    6.5088757
Afijio              31.1320755   30.1886792   38.6792453    0.0000000
Afikpo North        15.5913978   52.6881720   31.7204301    1.0752688
Afikpo South        37.5000000   43.7500000   18.7500000    3.1250000
Agaie                0.0000000   53.7634409   46.2365591    0.0000000
Agatu                2.4691358   88.8888889    8.6419753    0.0000000
Agege                6.6666667    0.0000000   91.6666667    0.0000000
Aguata              73.6842105    0.0000000   23.6842105    0.0000000
Agwara               1.7699115   84.9557522   13.2743363    0.0000000
Ahiazu-Mbaise       30.6666667    1.3333333   68.0000000    0.0000000
Ahoada East          0.0000000   15.7894737   84.2105263    0.0000000
Ahoada West          0.0000000    0.0000000  100.0000000    0.0000000
Aiyedade            19.8473282   50.3816794   27.2264631    0.0000000
Aiyedire            16.5714286   50.8571429   32.0000000    0.0000000
Aiyekire (Gbonyin)  20.9302326   58.1395349   20.9302326    4.0697674
Ajaokuta             0.0000000   44.6153846   55.3846154    0.0000000
Ajeromi-Ifelodun    37.5000000   50.0000000   12.5000000    0.0000000
Ajingi               0.0000000   94.0594059    5.9405941    0.0000000
Akamkpa             36.0000000   33.6000000   30.4000000    0.0000000
Akinyele            24.0223464   37.4301676   37.9888268    0.0000000
Akko                 0.0000000   76.9480519   23.0519481    0.0000000
Akoko-Edo            0.0000000   19.3548387   80.6451613    0.0000000
Akoko North East     0.0000000   47.5770925   48.0176211    0.0000000
Akoko North West     2.0833333   65.2777778   32.6388889    0.0000000
Akoko South East     0.0000000   48.0769231   49.3589744    0.0000000
Akoko South West     0.0000000   61.7449664   38.2550336    0.0000000
Akpabuyo            30.4687500   23.8281250   45.7031250    0.0000000
Akuku Toru          14.2857143   28.5714286   57.1428571    0.0000000
Akure North          0.0000000   70.2127660   29.7872340    0.0000000
Akure South          0.0000000   40.0000000   56.6666667    0.0000000
Akwanga              0.5555556   62.7777778   36.6666667    0.0000000
Albasu               0.0000000   91.2328767    8.4931507    0.0000000
Aleiro               0.0000000   40.9523810   59.0476190    0.0000000
Alimosho            22.8395062    1.5432099   75.6172840    0.0000000
Alkaleri             0.0000000   90.6250000    9.3750000    0.0000000
Amuwo-Odofin        30.0000000    5.0000000   65.0000000    0.0000000
Anambra East        52.1739130   47.8260870    0.0000000    0.0000000
Anambra West        20.3703704   46.2962963   31.4814815    0.0000000
Anaocha             47.9452055    0.0000000   52.0547945    0.0000000
Andoni               0.0000000   11.7647059   88.2352941    0.0000000
Aninri              60.0000000   40.0000000    0.0000000    0.0000000
Aniocha North        0.0000000    0.0000000  100.0000000    0.0000000
Aniocha South       15.3846154   30.7692308   53.8461538    0.0000000
Anka                 0.0000000   84.0000000   16.0000000    0.0000000
Ankpa                3.7037037   27.7777778   66.6666667    0.0000000
Apa                  7.6923077   79.4871795   12.8205128    0.0000000
Apapa              100.0000000    0.0000000    0.0000000    0.0000000
Ardo-Kola           16.5876777   72.9857820   10.4265403    0.0000000
Arewa-Dandi          0.0000000   29.0000000   71.0000000    0.0000000
Argungu              0.0000000   63.0769231   36.9230769    0.0000000
Arochukwu           44.0000000    8.0000000   40.0000000    0.0000000
Asa                  7.0422535   66.1971831   26.7605634    0.0000000
Asari-Toru           0.0000000    4.2553191   87.2340426    0.0000000
Askira/Uba           0.0000000   66.6666667   30.5555556    0.0000000
Atakumosa East      14.7982063   54.7085202   30.4932735    0.0000000
Atakumosa West      13.0081301   61.3821138   24.7967480    0.0000000
Atiba               32.1428571   21.4285714   46.4285714    0.0000000
Atigbo              22.3214286   47.3214286   30.3571429    0.0000000
Augie                0.0000000   79.1044776   19.4029851    0.0000000
Auyo                 0.0000000   99.4604317    0.5395683    0.0000000
Awe                  3.2258065   59.1397849   37.6344086    0.0000000
Awgu                26.9230769   50.0000000   19.2307692    0.0000000
Awka North          77.1428571    0.0000000   22.8571429    0.0000000
Awka South          55.0000000   15.0000000   30.0000000    0.0000000
Ayamelum            24.2424242   30.3030303   45.4545455    0.0000000
Babura               0.0000000   85.0111857   14.9888143    0.0000000
Badagry             18.0952381   10.4761905   71.4285714    0.0000000
Bade                 0.0000000   67.4050633   32.2784810    0.0000000
Bagudo               0.0000000   79.6116505   19.4174757    0.0000000
Bagwai               0.0000000   93.2142857    6.7857143    0.0000000
Bakassi              0.0000000    0.0000000    0.0000000    0.0000000
Bakori               0.0000000   81.6618911   18.3381089    0.0000000
Bakura               0.0000000   70.5128205   24.3589744    0.0000000
Balanga              0.0000000   84.1509434   15.4716981    0.0000000
Bali                12.0996441   72.2419929   15.6583630    0.0000000
Bama               100.0000000   98.9898990    1.0101010    0.0000000
Barikin Ladi        37.9790941   48.0836237   13.2404181    0.0000000
Baruten              4.2944785   80.9815951   14.7239264    0.6134969
Bassa Kogi           0.0000000   74.6031746   25.3968254    0.0000000
Bassa Plateau       41.2060302   43.2160804   15.5778894    0.0000000
Batagarawa           0.0000000   76.3358779   22.9007634    0.0000000
Batsari              0.0000000   80.6250000   19.3750000    0.0000000
Bauchi               0.0000000   87.0967742   12.9032258    0.0000000
Baure                0.0000000   74.0740741   22.8956229    0.0000000
Bayo                 0.0000000   79.1666667   20.8333333    0.0000000
Bebeji               0.0000000   96.3414634    3.6585366    0.0000000
Bekwara             32.4607330   44.5026178   23.0366492    0.0000000
Bende               50.0000000    6.6666667   43.3333333    0.0000000
Biase               33.5877863   36.6412214   29.7709924    0.0000000
Bichi                0.0000000   94.0828402    5.9171598    0.0000000
Bida                 0.0000000    5.3941909   94.1908714    0.0000000
Billiri              0.0000000   83.5978836   16.4021164    0.0000000
Bindawa              0.0000000   90.4059041    9.5940959    0.0000000
Binji                0.0000000   57.7319588   42.2680412    0.0000000
Biriniwa             0.0000000   98.4536082    1.1597938    0.0000000
Birni Kudu           0.0000000  100.0000000    0.0000000    0.0000000
Birnin-Gwari         0.0000000   88.0239521   11.9760479    0.0000000
Birnin Kebbi         1.6666667   24.1666667   75.8333333    0.0000000
Birnin Magaji        0.0000000   86.5979381   13.4020619    0.0000000
Biu                  0.0000000   72.6027397   27.3972603    0.0000000
Bodinga              0.0000000   26.5306122   73.4693878    0.0000000
Bogoro               0.8064516   90.3225806    8.8709677    0.0000000
Boki                24.0223464   55.8659218   20.1117318    0.0000000
Bokkos              41.6666667   44.2982456   14.0350877    0.0000000
Boluwaduro          11.6279070   55.8139535   32.5581395    0.0000000
Bomadi               0.0000000   75.0000000   25.0000000    0.0000000
Bonny                0.0000000  100.0000000    0.0000000    0.0000000
Borgu                2.7777778   81.4814815   15.7407407    0.0000000
Boripe               7.3446328   67.2316384   25.4237288    0.0000000
Bosso                4.5112782   87.9699248    6.7669173    0.0000000
Brass                0.0000000   13.6363636   86.3636364    0.0000000
Buji                 0.0000000   98.4802432    1.5197568    0.0000000
Bukkuyum             0.0000000   94.4444444    5.5555556    0.0000000
Bungudu              0.0000000   74.2857143   25.7142857    0.0000000
Bunkure              0.0000000   90.5511811    9.4488189    0.0000000
Bunza                0.0000000   68.7116564   31.2883436    0.0000000
Bursari              0.0000000   38.1443299   61.8556701    0.0000000
Buruku              21.0227273   64.7727273   13.6363636    0.0000000
Burutu              14.2857143   42.8571429   42.8571429    0.0000000
Bwari                1.6949153   53.3898305   44.9152542    0.0000000
Calabar-Municipal   47.5609756    6.0975610   46.3414634    0.0000000
Calabar South        8.2191781   49.3150685   42.4657534    0.0000000
Chanchaga            0.0000000   95.8333333    4.1666667    0.0000000
Charanchi            0.0000000   86.8020305   12.1827411    0.0000000
Chibok               0.0000000   60.6060606   36.3636364    0.0000000
Chikun               0.0000000  100.0000000    0.0000000    0.0000000
Dala                 0.0000000   57.2463768   42.7536232    0.0000000
Damaturu             0.0000000    0.0000000  100.0000000    0.0000000
Damban               0.0000000   85.1351351   14.8648649    0.0000000
Dambatta             0.0000000   82.2660099   17.7339901    0.0000000
Damboa             100.0000000  100.0000000    0.0000000    0.0000000
Dan Musa             0.0000000   76.5625000   23.4375000    0.0000000
Dandi                0.0000000   72.7272727   27.2727273    0.0000000
Dandume              0.0000000   76.5217391   23.4782609    0.0000000
Dange-Shuni          0.0000000   36.3636364   63.6363636    0.0000000
Danja                0.0000000   62.9629630   37.0370370    0.0000000
Darazo               0.0000000   92.2222222    7.7777778    0.0000000
Dass                 0.0000000   95.6521739    4.3478261    0.0000000
Daura                0.0000000   63.1578947   36.8421053    0.0000000
Dawakin Kudu         0.0000000   90.5213270    9.4786730    0.0000000
Dawakin Tofa         0.0000000   98.6013986    1.3986014    0.0000000
Degema              12.5000000   12.5000000   75.0000000    0.0000000
Dekina               0.0000000   10.5769231   89.4230769    0.0000000
Demsa                0.0000000   84.6153846    7.6923077    0.0000000
Dikwa              100.0000000   95.0000000    5.0000000    0.0000000
Doguwa               0.0000000   94.5454545    5.4545455    0.0000000
Doma                 1.1363636   25.0000000   75.0000000    0.0000000
Donga               12.4324324   75.1351351   12.4324324    0.0000000
Dukku                0.0000000   77.2058824   22.7941176    0.0000000
Dunukofia           11.1111111    4.4444444   84.4444444    0.0000000
Dutse                0.0000000   93.5064935    6.4935065    0.0000000
Dutsi                0.0000000   70.0000000   27.5000000    0.0000000
Dutsin-Ma            0.0000000   88.0258900   11.9741100    0.0000000
Eastern Obolo        0.0000000   24.0000000   76.0000000    0.0000000
Ebonyi              57.6923077   36.5384615    5.7692308    5.0000000
Edati                0.5319149   53.1914894   46.2765957    0.0000000
Ede North           11.5740741   51.8518519   36.1111111    0.0000000
Ede South           23.9726027   64.3835616   10.9589041    0.0000000
Edu                  0.0000000   68.7500000   31.2500000    0.0000000
Efon                28.8888889   44.4444444   26.6666667   17.7777778
Egbado North        41.6666667    6.6666667   46.6666667    0.0000000
Egbado South        12.5000000    3.1250000   84.3750000    0.0000000
Egbeda              31.9587629   49.4845361   18.5567010    0.0000000
Egbedore            23.8993711   55.9748428   20.1257862    0.0000000
Egor                 0.0000000    0.0000000  100.0000000    0.0000000
Ehime-Mbano          4.2553191    6.3829787   89.3617021    0.0000000
Ejigbo               8.2304527   83.7448560    7.8189300    0.0000000
Ekeremor             0.0000000   18.1818182   81.8181818    0.0000000
Eket                 0.0000000    8.8235294   91.1764706    0.0000000
Ekiti                0.0000000   63.3663366   35.1485149    0.0000000
Ekiti East          31.1111111   54.4444444   13.3333333   26.6666667
Ekiti South West    11.1111111   50.6172840   38.2716049    6.7901235
Ekiti West          16.2393162   54.2735043   29.0598291    7.6923077
Ekwusigo            86.1111111    0.0000000   13.8888889    0.0000000
Eleme                0.0000000   50.0000000   50.0000000    0.0000000
Emohua               0.0000000   83.3333333   16.6666667    0.0000000
Emure               27.6923077   41.5384615   30.7692308   13.8461538
Enugu East          21.7391304   34.7826087   43.4782609    0.0000000
Enugu North         12.5000000   37.5000000   16.6666667    0.0000000
Enugu South         52.6315789   31.5789474   42.1052632    0.0000000
Epe                 15.4589372   11.1111111   70.0483092    0.4830918
Esan Central        15.6250000    6.2500000   78.1250000    0.0000000
Esan North East      0.0000000    5.8823529   94.1176471    0.0000000
Esan South East      0.0000000    7.1428571   92.8571429    0.0000000
Esan West            0.0000000   20.5882353   79.4117647    0.0000000
Ese-Odo              0.0000000   18.9189189   81.0810811    0.0000000
Esit - Eket          0.0000000   28.9473684   71.0526316    0.0000000
Essien Udim          0.0000000    3.4482759   96.5517241    0.0000000
Etche               31.5789474    5.2631579   63.1578947    0.0000000
Ethiope East         0.0000000    9.0909091   86.3636364    0.0000000
Ethiope West         0.0000000   12.0000000   80.0000000    0.0000000
Eti-Osa             21.0526316    0.0000000   78.9473684    0.0000000
Etim Ekpo            0.0000000    9.8039216   90.1960784    0.0000000
Etinan               0.0000000   20.0000000   80.0000000    0.0000000
Etsako Central       4.1666667   20.8333333   75.0000000    0.0000000
Etsako East          2.3809524    7.1428571   90.4761905    0.0000000
Etsako West          0.0000000    0.0000000  100.0000000    0.0000000
Etung               21.2121212   41.2121212   37.5757576    0.0000000
Ewekoro             55.5555556    1.3888889   43.0555556    0.0000000
Ezeagu              64.7058824    0.0000000   35.2941176    0.0000000
Ezinihitte          39.2857143    0.0000000   60.7142857    0.0000000
Ezza North          41.2087912   56.3186813    2.4725275    2.1978022
Ezza South          37.8980892   57.3248408    4.7770701    3.8216561
Fagge                0.0000000   84.1269841   15.8730159    0.0000000
Fakai                0.0000000   85.4545455   14.5454545    0.0000000
Faskari              0.0000000   85.2173913   14.2028986    0.0000000
Fika                 0.0000000    0.0000000  100.0000000    0.0000000
Fufore               0.0000000   46.1538462   53.8461538    0.0000000
Funakaye             0.0000000   73.6842105   26.3157895    0.0000000
Fune                 0.0000000    1.4084507   98.5915493    0.0000000
Funtua               0.0000000   62.8571429   37.1428571    0.0000000
Gabasawa             0.0000000   84.3137255   15.6862745    0.0000000
Gada                 0.0000000   66.1016949   33.8983051    0.0000000
Gagarawa             0.0000000   99.1011236    0.8988764    0.0000000
Gamawa               0.0000000   95.0338600    4.9661400    0.0000000
Ganjuwa              0.0000000   95.0617284    4.9382716    0.0000000
Ganye                0.0000000   71.4285714   28.5714286    0.0000000
Garki                0.0000000   96.9976905    2.7713626    0.0000000
Garko                0.0000000   84.2696629   14.6067416    0.0000000
Garum Mallam         0.0000000   92.6380368    7.3619632    0.0000000
Gashaka             13.3333333   77.5757576    9.0909091    0.0000000
Gassol              21.5094340   63.7735849   14.3396226    0.0000000
Gaya                 0.0000000  100.0000000    0.0000000    0.0000000
Gbako                0.0000000   53.7142857   45.1428571    0.0000000
Gboko               24.2268041   53.0927835   26.8041237    0.0000000
Geidam               0.0000000    0.0000000    0.0000000    0.0000000
Gezawa               0.0000000   89.3992933   10.6007067    0.0000000
Giade                0.0000000   96.3636364    3.6363636    0.0000000
Girei                0.0000000   60.0000000   40.0000000    0.0000000
Giwa                 0.0000000   94.5736434    5.4263566    0.0000000
Gokana               0.0000000    0.0000000  100.0000000    0.0000000
Gombe                0.0000000   48.5714286   51.4285714    0.0000000
Gombi                0.0000000   77.7777778   22.2222222    0.0000000
Goronyo              0.0000000   57.1428571   42.8571429    0.0000000
Gubio                0.0000000    0.0000000    0.0000000    0.0000000
Gudu                 0.0000000   47.5000000   52.5000000    0.0000000
Gujba                0.0000000    0.0000000    0.0000000    0.0000000
Gulani               0.0000000    0.0000000  100.0000000    0.0000000
Guma                20.5128205   64.9572650   14.5299145    0.0000000
Gumel                0.0000000   62.4020888   37.5979112    0.0000000
Gummi                0.0000000   92.5373134    7.4626866    0.0000000
Gurara              25.6097561   62.1951220   12.1951220    0.0000000
Guri                 0.0000000   98.8338192    1.1661808    0.0000000
Gusau                0.0000000   87.5000000   12.5000000    0.0000000
Guyuk                0.0000000   59.0909091   40.9090909    0.0000000
Guzamala             0.0000000    0.0000000    0.0000000    0.0000000
Gwadabawa            0.0000000   69.2982456   30.7017544    0.0000000
Gwagwalada           1.6949153   61.0169492   38.1355932    0.0000000
Gwale                0.0000000   85.4838710   14.5161290    0.0000000
Gwandu               0.0000000   80.6122449   19.3877551    0.0000000
Gwaram               0.0000000   89.4230769    8.6538462    0.0000000
Gwarzo               0.0000000   86.8421053   11.8421053    0.0000000
Gwer East            3.9682540   79.3650794   15.8730159    0.0000000
Gwer West            4.7619048   76.1904762   19.0476190    0.0000000
Gwiwa                0.0000000   98.0198020    1.9801980    0.0000000
Gwoza              100.0000000   65.9090909   34.0909091    0.0000000
Hadejia              0.0000000   93.7500000    6.2500000    0.0000000
Hawul                0.0000000   65.6250000   34.3750000    0.0000000
Hong                 0.0000000   75.0000000   25.0000000    0.0000000
Ibadan North        33.3333333    6.0606061   60.6060606    0.0000000
Ibadan North East    7.0866142   30.7086614   62.2047244    0.0000000
Ibadan North West    4.5977011    5.7471264   88.5057471    0.0000000
Ibadan South East   22.2222222   19.4444444   55.5555556    2.7777778
Ibadan South West    8.4112150   23.3644860   68.2242991    0.0000000
Ibaji                0.0000000   38.2352941   61.7647059    0.0000000
Ibarapa Central     20.5741627   57.8947368   21.5311005    0.0000000
Ibarapa East        28.4313725   43.1372549   28.4313725    0.0000000
Ibarapa North       21.7171717   53.5353535   24.7474747    0.0000000
Ibeju/Lekki         17.2932331    3.7593985   78.9473684    0.0000000
Ibeno                0.0000000   25.0000000   75.0000000    0.0000000
Ibesikpo Asutan      0.0000000    1.8518519   98.1481481    0.0000000
Ibi                 13.4146341   78.0487805    8.5365854    0.0000000
Ibiono Ibom          0.0000000    7.8431373   92.1568627    0.0000000
Idah                 0.0000000   49.1525424   50.8474576    0.0000000
Idanre               0.9615385   86.5384615   12.5000000    0.0000000
Ideato North        64.2857143    0.0000000   35.7142857    0.0000000
Ideato South        43.4782609    0.0000000   56.5217391    0.0000000
Idemili North       85.7142857    2.0408163   12.2448980    0.0000000
Idemili South       80.3571429    3.5714286   16.0714286    0.0000000
Ido                 19.4174757   32.0388350   48.5436893    0.0000000
Ido-Osi             54.1666667   22.9166667   18.7500000   31.2500000
Ifako-Ijaye         16.3934426    1.6393443   81.9672131    0.0000000
Ife Central          2.5974026   52.5974026   44.8051948    0.0000000
Ife East             8.1218274   46.1928934   45.6852792    0.0000000
Ife North            7.2992701   52.5547445   39.4160584    0.0000000
Ife South           10.1123596   64.0449438   25.2808989    0.0000000
Ifedayo             22.6190476   48.8095238   28.5714286    0.0000000
Ifedore              0.0000000   74.1935484   25.8064516    0.0000000
Ifelodun Kwara       0.4991681   69.3843594   30.1164725    0.0000000
Ifelodun Osun        6.3492063   57.9365079   35.7142857    0.0000000
Ifo                 78.3132530    0.0000000   21.6867470    0.0000000
Igabi                0.0000000   93.1740614    6.8259386    0.0000000
Igalamela-Odolu      0.0000000   25.0000000   75.0000000    0.0000000
Igbo-Etiti          45.4545455    6.8181818   45.4545455    2.2727273
Igbo-Eze North      45.2380952    0.0000000   54.7619048    0.0000000
Igbo-Eze South      52.9411765    0.0000000   47.0588235    0.0000000
Igueben              0.0000000    0.0000000  100.0000000    0.0000000
Ihiala              56.4885496   43.5114504    0.0000000    0.0000000
Ihitte/Uboma        27.7777778    5.5555556   58.3333333    8.3333333
Ijebu East          77.4193548    3.2258065   19.3548387    0.0000000
Ijebu North         25.0000000   20.3125000   48.4375000    6.2500000
Ijebu North East    24.4444444    4.4444444   71.1111111    0.0000000
Ijebu Ode           20.6896552    5.1724138   74.1379310    0.0000000
Ijero               20.8737864   54.3689320   24.2718447   16.9902913
Ijumu                0.0000000   49.1803279   50.0000000    0.0000000
Ika                  0.0000000   14.2857143   85.7142857    0.0000000
Ika North East       0.0000000    0.0000000  100.0000000    0.0000000
Ika South            0.0000000    0.0000000  100.0000000    0.0000000
Ikara                0.0000000   86.8613139   13.1386861    0.0000000
Ikeduru             53.3333333    0.0000000   46.6666667    0.0000000
Ikeja               33.3333333   14.2857143   42.8571429    0.0000000
Ikenne              52.6315789    0.0000000   47.3684211    0.0000000
Ikere               10.3092784   62.8865979   26.8041237    4.1237113
Ikole               29.7297297   52.9729730   17.2972973   22.7027027
Ikom                26.3681592   40.2985075   33.3333333    0.0000000
Ikono                0.0000000    6.3492063   93.6507937    0.0000000
Ikorodu             19.1666667    2.0833333   70.0000000    8.7500000
Ikot Abasi           0.0000000   15.5172414   84.4827586    0.0000000
Ikot Ekpene          0.0000000    2.1276596   97.8723404    0.0000000
Ikpoba-Okha          0.0000000    1.5151515   98.4848485    0.0000000
Ikwerre              0.0000000   45.4545455   54.5454545    0.0000000
Ikwo                63.5220126   33.3333333    3.1446541    7.5471698
Ikwuano             47.6635514    4.6728972   47.6635514    0.0000000
Ila                 17.9190751   47.3988439   34.1040462    0.5780347
Ilaje                0.0000000    0.0000000  100.0000000    0.0000000
Ile-Oluji-Okeigbo    0.7194245   56.8345324   42.4460432    0.0000000
Ilejemeji           23.8095238   47.6190476   26.1904762   14.2857143
Ilesha East         16.5467626   30.9352518   52.5179856    0.0000000
Ilesha West          4.1237113   53.6082474   42.2680412    0.0000000
Illela               0.0000000   47.7272727   52.2727273    0.0000000
Ilorin East          0.0000000   64.8648649   35.1351351    0.0000000
Ilorin South         0.0000000   66.6666667   33.3333333    0.0000000
Ilorin West          0.0000000   52.9411765   46.4052288    0.0000000
Imeko-Afon          87.8787879    0.0000000   12.1212121    0.0000000
Ingawa               0.0000000   92.4242424    7.0707071    0.0000000
Ini                  0.0000000   21.7391304   78.2608696    0.0000000
Ipokia              25.3968254    3.1746032   71.4285714   12.6984127
Irele                0.0000000   14.9425287   85.0574713    0.0000000
Irepo               25.4237288   36.4406780   38.1355932    0.0000000
Irepodun Kwara       0.0000000   61.5384615   37.6923077    0.0000000
Irepodun Osun       12.5000000   31.7307692   54.8076923    0.0000000
Irepodun/Ifelodun   37.1681416   37.1681416   25.6637168   31.8584071
Irewole              9.9099099   45.9459459   44.1441441    0.0000000
Isa                  0.0000000   58.4905660   39.6226415    0.0000000
Ise/Orun            31.4285714   45.7142857   22.8571429    8.5714286
Iseyin              63.2075472    9.4339623   27.3584906    0.0000000
Ishielu             35.6557377   58.1967213    6.1475410    5.7377049
Isi-Uzo             24.1379310   75.8620690   12.0689655    0.0000000
Isiala-Ngwa North   41.5730337   16.8539326   41.5730337    0.0000000
Isiala-Ngwa South   23.4375000    3.1250000   73.4375000    0.0000000
Isiala Mbano        13.1578947    0.0000000   86.8421053    0.0000000
Isin                 2.5806452   57.4193548   40.0000000    0.6451613
Isiukwuato          32.7868852    8.1967213   59.0163934    0.0000000
Isokan              19.5121951   54.4715447   26.0162602    0.0000000
Isoko North          0.0000000    7.6923077   92.3076923    0.0000000
Isoko South          0.0000000   12.5000000   87.5000000    0.0000000
Isu                 62.5000000    0.0000000   37.5000000    0.0000000
Itas/Gadau           0.0000000   87.4125874   12.5874126    0.0000000
Itesiwaju           33.8028169   19.7183099   46.4788732    0.0000000
Itu                  1.6666667    5.0000000   93.3333333    0.0000000
Ivo                 45.4545455   38.6363636   15.9090909    4.5454545
Iwajowa             21.1764706   50.1960784   28.6274510    0.0000000
Iwo                 20.6250000   34.3750000   45.0000000    0.0000000
Izzi                68.6520376   28.2131661    3.1347962    1.8808777
Jaba                 0.0000000   96.7320261    3.2679739    0.0000000
Jada                 0.0000000   57.1428571   42.8571429    0.0000000
Jahun                0.0000000   95.8333333    4.1666667    0.0000000
Jakusko              0.0000000   17.1428571   80.0000000    0.0000000
Jalingo             10.8695652   76.8115942   12.3188406    0.0000000
Jama'are             0.0000000   91.2698413    8.7301587    0.0000000
Jega                 0.0000000   39.8648649   60.1351351    0.0000000
Jema'a               0.0000000   91.8088737    8.1911263    0.0000000
Jere                25.6578947   43.4210526   55.9210526    0.0000000
Jibia                0.0000000   83.1578947   16.8421053    0.0000000
Jos East            41.7910448   46.7661692   10.9452736    0.0000000
Jos North           27.6923077   36.9230769   35.3846154    0.0000000
Jos South           31.0160428   54.0106952   10.1604278    0.0000000
Kabba/Bunu           0.0000000   63.0136986   36.9863014    0.0000000
Kabo                 0.0000000  100.0000000    0.0000000    0.0000000
Kachia               0.0000000   95.6730769    3.8461538    0.0000000
Kaduna North         0.0000000   77.5862069   22.4137931    0.0000000
Kaduna South         0.0000000   93.2432432    6.7567568    0.0000000
Kafin Hausa          0.0000000  100.0000000    0.0000000    0.0000000
Kafur                0.0000000   83.7398374   16.2601626    0.0000000
Kaga                 0.0000000    0.0000000    0.0000000    0.0000000
Kagarko              0.0000000   96.2162162    3.7837838    0.0000000
Kaiama               4.8543689   85.4368932    9.7087379    0.0000000
Kaita                0.0000000   80.0664452   19.9335548    0.0000000
Kajola              21.9101124   49.4382022   28.6516854    0.0000000
Kajuru               0.0000000   95.1048951    4.8951049    0.0000000
Kala/Balge           0.0000000    0.0000000    0.0000000    0.0000000
Kalgo                0.0000000   60.0000000   40.0000000    0.0000000
Kaltungo             0.0000000   82.0000000   18.0000000    0.0000000
Kanam               42.0689655   50.0000000    6.8965517    0.0000000
Kankara              0.0000000   68.5039370   31.4960630    0.0000000
Kanke               31.6964286   61.6071429    6.6964286    0.0000000
Kankia               0.0000000   82.0143885   17.9856115    0.0000000
Kano Municipal       0.0000000   69.3965517   30.6034483    0.0000000
Karasuwa             0.0000000   66.1764706   33.8235294    0.0000000
Karaye               0.0000000  100.0000000    0.0000000    0.0000000
Karim-Lamido        18.5185185   62.3456790   19.1358025    0.0000000
Karu                 7.8651685   33.7078652   58.4269663    0.0000000
Katagum              0.0000000   72.8813559   27.1186441    0.0000000
Katcha              17.3913043   71.7391304   10.8695652    0.0000000
Katsina              0.0000000   57.9545455   42.0454545    0.0000000
Katsina-Ala         33.5443038   55.6962025   10.7594937    0.0000000
Kaugama              0.0000000   99.4555354    0.5444646    0.0000000
Kaura                0.0000000   91.4893617    8.5106383    0.0000000
Kaura Namoda         0.0000000   89.4736842   10.5263158    0.0000000
Kauru                0.0000000   98.2658960    1.7341040    0.0000000
Kazaure              0.0000000   56.3968668   43.6031332    0.0000000
Keana                0.0000000   63.6363636   36.3636364    0.0000000
Kebbe                0.0000000   64.0625000   34.3750000    0.0000000
Keffi                0.0000000   22.0779221   77.9220779    0.0000000
Khana                6.6666667    0.0000000   93.3333333    0.0000000
Kibiya               0.0000000   93.4272300    3.7558685    0.4694836
Kirfi               10.3448276   80.4597701   18.3908046    1.1494253
Kiri Kasamma         0.0000000   55.5110220   44.2885772    0.0000000
Kiru                 1.3215859   91.1894273    8.8105727    0.0000000
Kiyawa               0.0000000   91.8478261    1.6304348    0.0000000
Kogi                 0.0000000   52.8571429   45.7142857    0.0000000
Koko/Besse           0.0000000   80.4054054   19.5945946    0.0000000
Kokona               0.9523810   67.6190476   31.4285714    0.0000000
Kolokuma/Opokuma     5.0000000    0.0000000   95.0000000    0.0000000
Konduga             33.3333333   66.6666667   33.3333333    0.0000000
Konshisha           29.3628809   65.9279778    4.9861496    0.0000000
Kontagora            2.4691358   92.5925926    4.9382716    0.0000000
Kosofe              13.7931034   29.3103448   56.8965517    0.0000000
Kubau                0.0000000   95.8333333    4.1666667    0.0000000
Kudan                0.0000000  100.0000000    0.0000000    0.0000000
Kuje                 8.3916084   28.6713287   62.9370629    0.0000000
Kukawa               0.0000000    0.0000000    0.0000000    0.0000000
Kumbotso             0.0000000   92.1568627    7.8431373    0.0000000
Kunchi               0.0000000   97.6562500    2.3437500    0.0000000
Kura                 0.0000000   89.3129771   10.6870229    0.0000000
Kurfi                0.0000000   86.4077670   13.5922330    0.0000000
Kurmi               15.3061224   76.5306122    7.1428571    0.0000000
Kusada               0.0000000   88.2352941   11.7647059    0.0000000
Kwali                1.1235955   55.0561798   43.8202247    0.0000000
Kwami                0.0000000   77.3333333   22.6666667    0.0000000
Kwande              12.6436782   73.5632184   13.7931034    0.0000000
Kware                0.0000000   42.7480916   51.9083969    0.0000000
Kwaya Kusar          0.0000000  100.0000000    0.0000000    0.0000000
Lafia                4.0590406   30.9963100   64.9446494    0.3690037
Lagelu              16.8000000   44.0000000   39.2000000    0.0000000
Lagos Island         0.0000000   13.5135135   86.4864865    0.0000000
Lagos Mainland      26.1904762    2.3809524   69.0476190    0.0000000
Lamurde              0.0000000  100.0000000    0.0000000    0.0000000
Langtang North      34.1333333   59.7333333    6.1333333    0.0000000
Langtang South      35.8695652   44.5652174   18.4782609    0.0000000
Lapai                0.8474576   61.8644068   37.2881356    0.0000000
Lau                 14.2045455   75.0000000   10.7954545    0.0000000
Lavun                1.0000000   44.0000000   55.0000000    0.0000000
Lere                 0.0000000   98.3695652    1.6304348    0.0000000
Logo                31.2925170   56.4625850   12.9251701    0.0000000
Lokoja               0.0000000   52.6315789   47.3684211    0.0000000
Machina              0.0000000   72.9729730   27.0270270    0.0000000
Madagali             0.0000000    0.0000000    0.0000000    0.0000000
Madobi               0.0000000   96.0000000    1.1428571    0.0000000
Mafa               100.0000000    0.0000000  100.0000000    0.0000000
Magama               5.0000000   92.0000000    3.0000000    0.0000000
Magumeri             0.0000000   77.7777778   22.2222222    0.0000000
Mai'adua             0.0000000   62.5498008   37.4501992    0.0000000
Maiduguri           31.5789474   16.5413534   82.7067669    0.0000000
Maigatari            0.0000000   93.5294118    6.4705882    0.0000000
Maiha                0.0000000   82.3529412   17.6470588    0.0000000
Maiyama              0.0000000   66.0000000   34.0000000    0.0000000
Makoda               0.0000000   19.8113208   80.1886792    0.0000000
Makurdi              3.2608696   63.0434783   33.6956522    0.0000000
Malam Madori         0.0000000   98.4126984    1.5873016    0.0000000
Malumfashi           0.0000000   85.4166667   12.5000000    0.0000000
Mangu               33.8607595   53.7974684   12.0253165    0.0000000
Mani                 0.0000000   81.3793103   15.1724138    0.0000000
Maradun              0.0000000   78.1250000   20.8333333    0.0000000
Mariga               0.8474576   87.2881356   11.8644068    0.0000000
Markafi              0.0000000   91.2280702    8.7719298    0.0000000
Marte                0.0000000    0.0000000    0.0000000    0.0000000
Maru                 0.0000000   74.2514970   24.5508982    0.0000000
Mashegu              3.5714286   73.2142857   23.2142857    0.0000000
Mashi                0.0000000   62.2950820   36.8852459    0.0000000
Matazu               0.0000000   83.4196891   14.5077720    0.0000000
Mayo-Belwa           0.0000000   91.6666667    8.3333333    0.0000000
Mbaitoli            52.7272727    0.0000000   47.2727273    0.0000000
Mbo                  0.0000000   16.6666667   83.3333333    0.0000000
Michika              0.0000000   80.0000000   20.0000000    0.0000000
Miga                 0.0000000   95.3125000    4.6875000    0.0000000
Mikang              34.5762712   55.9322034    8.8135593    0.0000000
Minjibir             0.0000000   90.2912621    9.7087379    0.0000000
Misau                0.0000000   93.8931298    6.1068702    0.0000000
Mkpat Enin           0.0000000   28.6956522   71.3043478    0.0000000
Moba                38.1578947   40.7894737   21.0526316   32.8947368
Mobbar               0.0000000    0.0000000    0.0000000    0.0000000
Mokwa                0.0000000   42.1052632   57.8947368    0.0000000
Monguno            100.0000000   96.1538462    3.8461538    0.0000000
Mopa-Muro            0.0000000   66.2790698   33.7209302    0.0000000
Moro                 3.4285714   82.8571429   13.7142857    0.0000000
Mubi North           0.0000000    0.0000000  100.0000000    0.0000000
Mubi South           0.0000000    0.0000000  100.0000000    0.0000000
Musawa               0.0000000   90.9090909    8.3916084    0.0000000
Mushin               9.3750000    0.0000000   90.6250000    0.0000000
Muya                 0.0000000   93.6842105    6.3157895    0.0000000
Nafada               0.0000000   62.8378378   37.1621622    0.0000000
Nangere              0.0000000    5.4054054   91.8918919    0.0000000
Nasarawa Kano        0.0000000   48.0000000   52.0000000    0.0000000
Nasarawa             6.2068966   51.7241379   41.3793103    0.0000000
Nasarawa-Eggon       1.8867925   50.3144654   47.1698113    0.0000000
Ndokwa East          0.0000000   32.5301205   53.0120482   14.4578313
Ndokwa West          0.0000000   15.2173913   84.7826087    0.0000000
Nembe                0.0000000   18.6046512   74.4186047    2.3255814
Ngala              100.0000000   93.0000000    7.0000000    0.0000000
Nganzai              0.0000000    0.0000000    0.0000000    0.0000000
Ngaski               0.0000000   93.9130435    6.0869565    0.0000000
Ngor-Okpala         48.5294118    2.9411765   48.5294118    0.0000000
Nguru                0.0000000   88.0630631   11.9369369    0.0000000
Ningi                0.0000000   96.7391304    3.2608696    0.0000000
Njaba               46.6666667    0.0000000   53.3333333    0.0000000
Njikoka             29.4117647   41.1764706   29.4117647    0.0000000
Nkanu East          61.1111111   37.0370370    1.8518519    1.8518519
Nkanu West          32.9113924   44.3037975   21.5189873    1.2658228
Nkwerre              3.0303030    9.0909091   87.8787879    0.0000000
Nnewi North         43.5897436    2.5641026   53.8461538    0.0000000
Nnewi South         90.9090909    3.0303030    6.0606061    0.0000000
Nsit Atai            0.0000000   13.2352941   86.7647059    0.0000000
Nsit Ibom            0.0000000    8.1632653   91.8367347    0.0000000
Nsit Ubium           0.0000000    9.0909091   90.9090909    0.0000000
Nsukka              62.5000000    0.0000000   39.2857143    0.0000000
Numan                0.0000000    0.0000000  100.0000000    0.0000000
Nwangele            25.0000000    3.5714286   71.4285714    0.0000000
Obafemi-Owode       40.9090909    7.5757576   51.5151515    0.0000000
Obanliku            28.5714286   41.7582418   29.6703297    0.0000000
Nasarawa Nasarawa   21.6867470   69.8795181    7.8313253    0.0000000
Obi Nasarawa         0.0000000   53.4090909   46.5909091    0.0000000
Obi Ngwa            16.7630058   27.1676301   56.0693642    0.0000000
Obia/Akpor           0.0000000   25.6000000   72.8000000    0.0000000
Obokun               4.4554455   50.9900990   44.5544554    0.0000000
Obot Akara           3.1746032    7.9365079   88.8888889    0.0000000
Obowo               40.9090909    0.0000000   59.0909091    0.0000000
Obubra              16.3090129   57.5107296   26.1802575    1.2875536
Obudu               24.6861925   42.2594142   33.0543933    0.0000000
Odeda               51.3274336   16.8141593   30.9734513    0.0000000
Odigbo               0.0000000   50.0000000   50.0000000    0.0000000
Odo-Otin            15.4605263   50.9868421   33.5526316    0.0000000
Odogbolu            19.4805195    6.4935065   72.7272727    0.0000000
Odukpani             5.3892216   28.1437126   65.2694611    0.0000000
Offa                 0.0000000   60.1063830   39.3617021    0.0000000
Ofu                  0.0000000   20.5882353   79.4117647    0.0000000
Ogba/Egbema/Ndoni   24.1935484    6.4516129   69.3548387    0.0000000
Ogbadibo            19.7530864   20.9876543   60.4938272    0.0000000
Ogbaru              30.4347826   23.9130435   45.6521739    0.0000000
Ogbia                0.0000000   87.5000000   12.5000000    0.0000000
Ogbomosho North      3.0000000    9.0000000   88.0000000    0.0000000
Ogbomosho South      0.8620690   34.4827586   64.6551724    0.0000000
Ogo Oluwa            4.3010753   41.9354839   53.7634409    0.0000000
Ogoja               37.7289377   42.1245421   20.1465201    0.0000000
Ogori/Magongo        0.0000000   27.0833333   70.8333333    2.0833333
Ogu/Bolo             0.0000000    0.0000000  100.0000000    0.0000000
Ogun waterside       4.0540541    9.4594595   86.4864865    0.0000000
Oguta               23.2323232   27.2727273   49.4949495    0.0000000
Ohafia              28.9473684    7.8947368   63.1578947    0.0000000
Ohaji/Egbema        12.9213483   68.5393258   17.9775281    0.0000000
Ohaozara            35.6363636   53.0909091   11.2727273    2.9090909
Ohaukwu             39.7759104   56.8627451    3.6414566    5.0420168
Ohimini              0.0000000   90.7692308    9.2307692    0.0000000
Oji-River           52.6315789    2.6315789   44.7368421    0.0000000
Ojo                 45.9770115    3.4482759   50.5747126    0.0000000
Oju                  1.4652015   93.0402930    5.4945055    0.0000000
Oke-Ero              0.4830918   71.4975845   25.6038647    0.4830918
Okehi                0.0000000   59.4594595   39.1891892    0.0000000
Okene                0.0000000   19.0000000   80.0000000    0.0000000
Okigwe               9.7560976    0.0000000   87.8048780    0.0000000
Okitipupa            0.0000000   16.0000000   84.0000000    0.0000000
Okobo                1.3513514    8.1081081   90.5405405    0.0000000
Okpe                23.0769231    7.6923077   61.5384615    0.0000000
Okpokwu             13.5593220   86.4406780   10.1694915    0.0000000
Okrika               0.0000000   33.3333333   50.0000000   16.6666667
Ola-oluwa           39.1891892   41.8918919   18.9189189    0.0000000
Olamabolo            0.0000000    0.0000000  100.0000000    0.0000000
Olorunda            12.1546961   55.2486188   32.5966851    0.0000000
Olorunsogo          36.3636364   25.2525253   37.3737374    0.0000000
Oluyole             36.4864865   45.9459459   13.5135135    0.0000000
Omala                0.0000000   48.6486486   51.3513514    0.0000000
Omumma               0.0000000   40.0000000   60.0000000    0.0000000
Ona-Ara             22.8070175   42.9824561   34.2105263    0.0000000
Ondo East            0.0000000   58.5185185   41.4814815    0.0000000
Ondo West            0.0000000   51.5527950   48.4472050    0.0000000
Onicha              31.9634703   63.4703196    4.1095890    5.9360731
Onitsha North       84.6153846    0.0000000   15.3846154    0.0000000
Onitsha South       41.6666667    8.3333333   50.0000000    0.0000000
Onna                 0.0000000   19.0476190   80.9523810    0.0000000
Opobo/Nkoro          0.0000000   45.4545455   45.4545455    0.0000000
Oredo                0.0000000    4.3478261   95.6521739    0.0000000
Orelope             35.7142857   46.9387755   17.3469388    0.0000000
Orhionmwon           0.0000000    0.9009009   99.0990991    0.0000000
Ori Ire             41.8410042   43.0962343   15.0627615    0.0000000
Oriade              18.1467181   43.2432432   38.6100386    0.7722008
Orlu                35.2941176    5.8823529   58.8235294    0.0000000
Orolu                7.5000000   50.8333333   40.0000000    0.0000000
Oron                 0.0000000   20.0000000   80.0000000    0.0000000
Orsu                20.0000000    5.0000000   75.0000000    0.0000000
Oru East            61.5384615    0.0000000   38.4615385    0.0000000
Oru West            58.5365854    7.3170732   34.1463415    0.0000000
Oruk Anam            0.0000000   22.0338983   77.9661017    0.0000000
Orumba North        57.5757576   10.6060606   31.8181818    0.0000000
Orumba South        48.4848485    0.0000000   51.5151515    0.0000000
Ose                  0.0000000   46.0000000   52.0000000    0.0000000
Oshimili North       0.0000000    3.0303030   96.9696970    0.0000000
Oshimili South       0.0000000    8.6956522   91.3043478    0.0000000
Oshodi-Isolo        31.9148936    4.2553191   61.7021277    0.0000000
Osisioma Ngwa       43.1818182    6.8181818   70.4545455    0.0000000
Osogbo               8.7209302   36.0465116   54.0697674    0.0000000
Oturkpo             11.2903226   75.8064516   12.9032258    0.0000000
Ovia North East      8.4337349    0.0000000   91.5662651    0.0000000
Ovia South West      3.8961039    1.2987013   94.8051948    0.0000000
Owan East            1.5873016    6.3492063   92.0634921    0.0000000
Owan West            0.0000000    6.8181818   93.1818182    0.0000000
Owerri-Municipal    29.0322581   12.9032258   58.0645161    0.0000000
Owerri North        25.0000000    2.9411765   72.0588235    0.0000000
Owerri West         19.6721311   31.1475410   49.1803279    0.0000000
Owo                  0.0000000   46.4088398   53.5911602    0.0000000
Oye                 15.4929577   55.6338028   28.8732394    9.1549296
Oyi                 38.3561644    0.0000000   61.6438356    0.0000000
Oyigbo               0.0000000   41.1764706   58.8235294    0.0000000
Oyo East            26.1363636   30.6818182   43.1818182    0.0000000
Oyo West            53.1250000   10.9375000   35.9375000    0.0000000
Oyun                 0.0000000   70.7462687   28.3582090    0.0000000
Paikoro              1.0638298   88.2978723   10.6382979    0.0000000
Pankshin            42.9648241   48.7437186    8.2914573    0.0000000
Patani               0.0000000   18.1818182   72.7272727    0.0000000
Pategi               2.9090909   67.6363636   29.4545455    0.0000000
Port-Harcourt        0.0000000   69.5652174    5.2173913   23.4782609
Potiskum             0.0000000   25.0000000   75.0000000    0.0000000
Qua'an Pan          36.8115942   54.4927536    8.1159420    0.0000000
Rabah                0.0000000   58.7301587   41.2698413    0.0000000
Rafi                 0.0000000   87.1212121   12.8787879    0.0000000
Rano                 0.0000000   91.0000000    8.0000000    0.0000000
Remo North          29.5454545   13.6363636   56.8181818    0.0000000
Rijau                7.2847682   78.8079470   13.9072848    0.0000000
Rimi                 0.0000000   71.4285714   28.5714286    0.0000000
Rimin Gado           0.0000000   96.9879518    1.8072289    0.0000000
Ringim               0.0000000   87.5000000   11.8055556    0.0000000
Riyom               41.4473684   47.0394737   10.8552632    0.0000000
Rogo                 0.0000000   88.6486486   11.3513514    0.0000000
Roni                 0.0000000   94.6666667    5.3333333    0.0000000
Sabon-Gari           0.0000000   76.2626263   23.7373737    0.0000000
Sabon Birni          0.0000000   80.5194805   19.4805195    0.0000000
Sabuwa               0.0000000   74.4186047   23.2558140    0.0000000
Safana               0.0000000   93.7888199    4.9689441    0.0000000
Sagbama              0.0000000   20.9677419   74.1935484    4.8387097
Sakaba               0.0000000   89.6551724    9.6551724    0.0000000
Saki East           13.7614679   44.9541284   41.2844037    0.0000000
Saki West           22.9729730   48.6486486   27.7027027    0.0000000
Sandamu              0.0000000   70.6422018   23.8532110    0.0000000
Sanga                0.0000000   94.1558442    5.8441558    0.0000000
Sapele               0.0000000    8.3333333   91.6666667    0.0000000
Sardauna            16.0000000   74.0000000   10.0000000    0.0000000
Shagamu             35.2459016    1.6393443   63.1147541    0.0000000
Shagari              0.0000000   17.3076923   82.6923077    0.0000000
Shanga               0.0000000   85.4368932   14.5631068    0.0000000
Shani                0.0000000   63.4146341   36.5853659    0.0000000
Shanono              0.0000000   77.1084337   22.8915663    0.0000000
Shelleng             0.0000000   88.8888889   11.1111111    0.0000000
Shendam             48.1308411   45.0934579    6.7757009    0.0000000
Shinkafi             0.0000000   88.5350318   11.4649682    0.0000000
Shira                0.0000000   89.7560976   10.2439024    0.0000000
Shiroro             11.0091743   79.8165138    9.1743119    0.0000000
Shomgom              0.0000000   88.8446215   11.1553785    0.0000000
Shomolu             21.4285714    0.0000000   78.5714286    0.0000000
Silame               0.0000000   46.6666667   53.3333333    0.0000000
Soba                 0.0000000   91.7675545    8.2324455    0.0000000
Sokoto North         0.0000000   15.1515152   84.8484848    0.0000000
Sokoto South         0.0000000   32.0000000   68.0000000    0.0000000
Song                 0.0000000   78.5714286   21.4285714    0.0000000
Southern Ijaw        0.0000000    9.6774194   35.4838710    0.0000000
Sule-Tankarkar       0.0000000   89.8969072   10.1030928    0.0000000
Suleja               0.0000000   80.4878049   19.5121951    0.0000000
Sumaila              0.0000000   96.3898917    3.2490975    0.0000000
Suru                 0.0000000   80.0000000   20.0000000    0.0000000
Obi Benue            9.8360656   19.6721311   67.2131148    1.6393443
Surulere Lagos       3.1746032   52.3809524   41.7989418    0.0000000
Tafa                 0.0000000   68.7500000   31.2500000    0.0000000
Tafawa-Balewa        1.5625000   87.5000000   12.5000000    0.0000000
Tai                  0.0000000    0.0000000  100.0000000    0.0000000
Takai                0.0000000   93.0337079    6.9662921    0.0000000
Takum                7.7777778   76.6666667   15.0000000    0.0000000
Talata Mafara        0.0000000   72.1925134   27.8074866    0.0000000
Tambuwal             0.0000000   48.3146067   51.6853933    0.0000000
Tangaza              0.0000000   56.2500000   43.7500000    0.0000000
Tarauni              0.0000000   88.0597015   10.4477612    1.4925373
Tarka               19.6428571   74.1071429    6.2500000    0.0000000
Tarmua               0.0000000    5.5555556   94.4444444    0.0000000
Taura                0.0000000   98.7969925    0.9022556    0.0000000
Tofa                 0.0000000   89.1891892   10.8108108    0.0000000
Toro                 3.0075188   93.9849624    5.2631579    0.0000000
Toto                 1.7094017   35.8974359   62.3931624    0.0000000
Toungo               0.0000000   80.0000000   20.0000000    0.0000000
Tsafe                0.0000000   84.4221106   15.0753769    0.0000000
Tsanyawa             0.0000000   96.1290323    3.8709677    0.0000000
Tudun Wada           0.0000000   84.6153846   14.2011834    0.0000000
Tureta               0.0000000   58.5585586   41.4414414    0.0000000
Udenu               72.7272727    0.0000000   27.2727273    0.0000000
Udi                 67.6190476    0.9523810   28.5714286    0.0000000
Udu                  0.0000000   72.7272727   27.2727273    0.0000000
Udung Uko            0.0000000   11.1111111   88.8888889    0.0000000
Ughelli North        7.5471698   24.5283019   60.3773585    1.8867925
Ughelli South        0.0000000   32.0000000   68.0000000    0.0000000
Ugwunagbo           23.8636364   47.7272727   25.0000000    0.0000000
Uhunmwonde           3.1746032    3.1746032   92.0634921    0.0000000
Ukanafun             0.0000000   38.0952381   61.9047619    0.0000000
Ukum                26.5822785   63.2911392   10.1265823    0.0000000
Ukwa East            7.7777778   53.3333333   38.8888889    0.0000000
Ukwa West           18.5483871   26.6129032   54.8387097    0.0000000
Ukwuani              0.0000000   20.3703704   79.6296296    0.0000000
Umu-Nneochi         56.8181818    9.0909091   34.0909091    0.0000000
Umuahia North       35.1351351   16.2162162   47.2972973    0.0000000
Umuahia South       43.6893204    7.7669903   48.5436893    0.0000000
Ungogo               0.0000000   95.3642384    4.6357616    0.0000000
Unuimo              50.0000000   33.3333333   16.6666667    0.0000000
Uruan                0.0000000   10.4166667   85.4166667    0.0000000
Urue-Offong/Oruko    0.0000000   17.8571429   82.1428571    0.0000000
Ushongo             29.6943231   63.7554585    7.8602620    0.0000000
Ussa                10.8108108   73.5135135   15.6756757    0.0000000
Uvwie                0.0000000   18.1818182   63.6363636    0.0000000
Uyo                  0.0000000    5.0000000   95.0000000    0.0000000
Uzo-Uwani           88.0000000   16.0000000    8.0000000    0.0000000
Vandeikya           33.9285714   55.3571429   12.5000000    0.0000000
Wamako               0.0000000   35.8024691   64.1975309    0.0000000
Wamba                0.9803922   49.0196078   48.0392157    0.0000000
Warawa               0.0000000   97.4874372    2.5125628    0.0000000
Warji                0.0000000   98.5454545    1.4545455    0.0000000
Warri North          0.0000000    3.3333333   96.6666667    0.0000000
Warri South          0.0000000   53.8461538   44.2307692    0.0000000
Warri South West     0.0000000   28.5714286   57.1428571    0.0000000
Wasagu/Danko         0.0000000   76.0000000   24.0000000    0.0000000
Wase                34.2696629   37.6404494   27.5280899    0.0000000
Wudil                0.0000000   79.1666667   12.5000000    8.3333333
Wukari              19.3415638   74.0740741    6.5843621    0.0000000
Wurno                0.0000000   15.2542373   84.7457627    0.0000000
Wushishi             0.0000000   87.7697842   12.2302158    0.0000000
Yabo                 0.0000000   70.7317073   29.2682927    0.0000000
Yagba East           0.0000000   54.2372881   45.7627119    0.0000000
Yagba West           0.0000000   68.0672269   31.9327731    0.0000000
Yakurr              10.1538462   66.4615385   23.3846154    0.0000000
Yala                41.5929204   40.1179941   18.2890855    0.0000000
Yamaltu/Deba         0.0000000   81.0483871   18.9516129    0.0000000
Yankwashi            0.0000000   96.9026549    2.6548673    0.0000000
Yauri                0.0000000   88.4615385   11.5384615    0.0000000
Yenegoa              0.0000000   25.9259259   74.0740741    0.0000000
Yola North           0.0000000   34.6153846   65.3846154    0.0000000
Yola South           0.0000000   69.2307692   30.7692308    0.0000000
Yorro               16.0804020   74.8743719    9.0452261    0.0000000
Yunusari             0.0000000    0.0000000  100.0000000    0.0000000
Yusufari             0.0000000   35.7142857   64.2857143    0.0000000
Zaki                 0.0000000   94.3262411    5.6737589    0.0000000
Zango                0.0000000   52.3364486   47.6635514    0.0000000
Zango-Kataf          0.0000000   94.3444730    5.6555270    0.0000000
Zaria                0.0000000   94.9843260    5.0156740    0.0000000
Zing                11.0526316   77.3684211   11.0526316    0.0000000
Zurmi                0.0000000   79.0697674   20.1550388    0.0000000
Zuru                 0.0000000   90.0000000   10.0000000    0.0000000
                     pct_uc300  pct_uc1000 pct_ucN1000  pct_uc250  pct_urban0
Aba North           17.6470588  82.3529412  17.6470588  0.0000000   0.0000000
Aba South           12.6760563  87.3239437  12.6760563  0.0000000   5.6338028
Abadam               0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Abaji               40.3508772  59.6491228  40.3508772  0.0000000  84.2105263
Abak                 8.3333333  91.6666667   8.3333333  0.0000000  83.3333333
Abakaliki           75.1072961   9.4420601  90.5579399 15.4506438  87.5536481
Abeokuta North      23.5294118  76.4705882  23.5294118  0.0000000  20.5882353
Abeokuta South      28.5714286  70.5882353  29.4117647  0.8403361   0.0000000
Abi                 67.1052632  32.8947368  67.1052632  0.0000000  95.3947368
Aboh-Mbaise         34.8484848  65.1515152  34.8484848  0.0000000  72.7272727
Abua/Odual          33.3333333  66.6666667  33.3333333  0.0000000  53.8461538
Abuja Municipal     40.7407407  59.2592593  40.7407407  0.0000000  84.4444444
Adavi               46.0317460  53.9682540  46.0317460  0.0000000  28.5714286
Ado                 96.0937500   3.1250000  96.8750000  0.7812500  97.6562500
Ado-Odo/Ota         34.4827586  65.5172414  34.4827586  0.0000000  54.8850575
Ado Ekiti           60.3550296  33.1360947  66.8639053  6.5088757  27.8106509
Afijio              61.3207547  38.6792453  61.3207547  0.0000000  72.6415094
Afikpo North        67.2043011  31.7204301  68.2795699  1.0752688  72.0430108
Afikpo South        78.1250000  18.7500000  81.2500000  3.1250000  82.8125000
Agaie               53.7634409  46.2365591  53.7634409  0.0000000  20.4301075
Agatu               91.3580247   8.6419753  91.3580247  0.0000000 100.0000000
Agege                8.3333333  91.6666667   8.3333333  0.0000000   0.0000000
Aguata              76.3157895  23.6842105  76.3157895  0.0000000  65.7894737
Agwara              86.7256637  13.2743363  86.7256637  0.0000000 100.0000000
Ahiazu-Mbaise       32.0000000  68.0000000  32.0000000  0.0000000  24.0000000
Ahoada East         15.7894737  84.2105263  15.7894737  0.0000000  73.6842105
Ahoada West          0.0000000 100.0000000   0.0000000  0.0000000  78.5714286
Aiyedade            72.7735369  27.2264631  72.7735369  0.0000000  83.4605598
Aiyedire            68.0000000  32.0000000  68.0000000  0.0000000  81.7142857
Aiyekire (Gbonyin)  75.0000000  20.9302326  79.0697674  4.0697674 100.0000000
Ajaokuta            44.6153846  55.3846154  44.6153846  0.0000000 100.0000000
Ajeromi-Ifelodun    87.5000000  12.5000000  87.5000000  0.0000000   0.0000000
Ajingi              94.0594059   5.9405941  94.0594059  0.0000000 100.0000000
Akamkpa             69.6000000  30.4000000  69.6000000  0.0000000 100.0000000
Akinyele            62.0111732  37.9888268  62.0111732  0.0000000  75.9776536
Akko                76.9480519  23.0519481  76.9480519  0.0000000  98.7012987
Akoko-Edo           19.3548387  80.6451613  19.3548387  0.0000000  87.0967742
Akoko North East    51.9823789  48.0176211  51.9823789  0.0000000  33.9207048
Akoko North West    67.3611111  32.6388889  67.3611111  0.0000000  81.9444444
Akoko South East    50.6410256  49.3589744  50.6410256  0.0000000 100.0000000
Akoko South West    61.7449664  38.2550336  61.7449664  0.0000000  47.9865772
Akpabuyo            54.2968750  45.7031250  54.2968750  0.0000000 100.0000000
Akuku Toru          42.8571429  57.1428571  42.8571429  0.0000000  64.2857143
Akure North         70.2127660  29.7872340  70.2127660  0.0000000  97.8723404
Akure South         43.3333333  56.6666667  43.3333333  0.0000000  10.0000000
Akwanga             63.3333333  36.6666667  63.3333333  0.0000000  79.4444444
Albasu              91.5068493   8.4931507  91.5068493  0.0000000  90.9589041
Aleiro              40.9523810  59.0476190  40.9523810  0.0000000 100.0000000
Alimosho            24.3827160  75.6172840  24.3827160  0.0000000   0.0000000
Alkaleri            90.6250000   9.3750000  90.6250000  0.0000000  86.4583333
Amuwo-Odofin        35.0000000  65.0000000  35.0000000  0.0000000  10.0000000
Anambra East       100.0000000   0.0000000 100.0000000  0.0000000 100.0000000
Anambra West        68.5185185  31.4814815  68.5185185  0.0000000 100.0000000
Anaocha             47.9452055  52.0547945  47.9452055  0.0000000  28.7671233
Andoni              11.7647059  88.2352941  11.7647059  0.0000000  64.7058824
Aninri             100.0000000   0.0000000 100.0000000  0.0000000  52.5000000
Aniocha North        0.0000000 100.0000000   0.0000000  0.0000000 100.0000000
Aniocha South       46.1538462  53.8461538  46.1538462  0.0000000 100.0000000
Anka                84.0000000  16.0000000  84.0000000  0.0000000 100.0000000
Ankpa               33.3333333  66.6666667  33.3333333  0.0000000  83.3333333
Apa                 87.1794872  12.8205128  87.1794872  0.0000000 100.0000000
Apapa              100.0000000   0.0000000 100.0000000  0.0000000   0.0000000
Ardo-Kola           89.5734597  10.4265403  89.5734597  0.0000000 100.0000000
Arewa-Dandi         29.0000000  71.0000000  29.0000000  0.0000000 100.0000000
Argungu             63.0769231  36.9230769  63.0769231  0.0000000  72.3076923
Arochukwu           56.0000000  40.0000000  60.0000000  4.0000000  84.0000000
Asa                 73.2394366  26.7605634  73.2394366  0.0000000  99.2957746
Asari-Toru          12.7659574  87.2340426  12.7659574  0.0000000  23.4042553
Askira/Uba          69.4444444  30.5555556  69.4444444  0.0000000 100.0000000
Atakumosa East      69.5067265  30.4932735  69.5067265  0.0000000 100.0000000
Atakumosa West      75.2032520  24.7967480  75.2032520  0.0000000  99.1869919
Atiba               53.5714286  46.4285714  53.5714286  0.0000000  40.1785714
Atigbo              69.6428571  30.3571429  69.6428571  0.0000000  82.1428571
Augie               80.5970149  19.4029851  80.5970149  0.0000000 100.0000000
Auyo                99.4604317   0.5395683  99.4604317  0.0000000  96.4028777
Awe                 62.3655914  37.6344086  62.3655914  0.0000000 100.0000000
Awgu                80.7692308  19.2307692  80.7692308  0.0000000  73.0769231
Awka North          77.1428571  22.8571429  77.1428571  0.0000000 100.0000000
Awka South          70.0000000  30.0000000  70.0000000  0.0000000  47.5000000
Ayamelum            54.5454545  45.4545455  54.5454545  0.0000000  66.6666667
Babura              85.0111857  14.9888143  85.0111857  0.0000000 100.0000000
Badagry             28.5714286  71.4285714  28.5714286  0.0000000  76.1904762
Bade                67.7215190  32.2784810  67.7215190  0.0000000  72.4683544
Bagudo              80.5825243  19.4174757  80.5825243  0.0000000  78.6407767
Bagwai              93.2142857   6.7857143  93.2142857  0.0000000  93.5714286
Bakassi              0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Bakori              81.6618911  18.3381089  81.6618911  0.0000000  86.5329513
Bakura              75.6410256  24.3589744  75.6410256  0.0000000 100.0000000
Balanga             84.5283019  15.4716981  84.5283019  0.0000000  86.4150943
Bali                84.3416370  15.6583630  84.3416370  0.0000000 100.0000000
Bama                98.9898990   1.0101010  98.9898990  0.0000000  18.1818182
Barikin Ladi        86.7595819  13.2404181  86.7595819  0.0000000  95.8188153
Baruten             84.6625767  14.7239264  85.2760736  0.6134969 100.0000000
Bassa Kogi          74.6031746  25.3968254  74.6031746  0.0000000 100.0000000
Bassa Plateau       84.4221106  15.5778894  84.4221106  0.0000000  98.9949749
Batagarawa          77.0992366  22.9007634  77.0992366  0.0000000  95.4198473
Batsari             80.6250000  19.3750000  80.6250000  0.0000000  84.3750000
Bauchi              87.0967742  12.9032258  87.0967742  0.0000000  68.3870968
Baure               76.7676768  22.8956229  77.1043771  0.0000000 100.0000000
Bayo                79.1666667  20.8333333  79.1666667  0.0000000 100.0000000
Bebeji              96.3414634   3.6585366  96.3414634  0.0000000 100.0000000
Bekwara             76.9633508  23.0366492  76.9633508  0.0000000 100.0000000
Bende               56.6666667  43.3333333  56.6666667  0.0000000  81.6666667
Biase               70.2290076  29.7709924  70.2290076  0.0000000 100.0000000
Bichi               94.0828402   5.9171598  94.0828402  0.0000000  95.2662722
Bida                 5.8091286  94.1908714   5.8091286  0.0000000   0.8298755
Billiri             83.5978836  16.4021164  83.5978836  0.0000000  91.0052910
Bindawa             90.4059041   9.5940959  90.4059041  0.0000000 100.0000000
Binji               57.7319588  42.2680412  57.7319588  0.0000000 100.0000000
Biriniwa            98.8402062   1.1597938  98.8402062  0.0000000 100.0000000
Birni Kudu         100.0000000   0.0000000 100.0000000  0.0000000  76.1006289
Birnin-Gwari        88.0239521  11.9760479  88.0239521  0.0000000  84.4311377
Birnin Kebbi        24.1666667  75.8333333  24.1666667  0.0000000  59.1666667
Birnin Magaji       86.5979381  13.4020619  86.5979381  0.0000000 100.0000000
Biu                 72.6027397  27.3972603  72.6027397  0.0000000  17.8082192
Bodinga             26.5306122  73.4693878  26.5306122  0.0000000  74.4897959
Bogoro              91.1290323   8.8709677  91.1290323  0.0000000 100.0000000
Boki                79.8882682  20.1117318  79.8882682  0.0000000 100.0000000
Bokkos              85.9649123  14.0350877  85.9649123  0.0000000  95.1754386
Boluwaduro          67.4418605  32.5581395  67.4418605  0.0000000  88.3720930
Bomadi              75.0000000  25.0000000  75.0000000  0.0000000 100.0000000
Bonny              100.0000000   0.0000000 100.0000000  0.0000000   0.0000000
Borgu               84.2592593  15.7407407  84.2592593  0.0000000  94.4444444
Boripe              74.5762712  25.4237288  74.5762712  0.0000000  19.2090395
Bosso               93.2330827   6.7669173  93.2330827  0.0000000  95.4887218
Brass               13.6363636  86.3636364  13.6363636  0.0000000  90.9090909
Buji                98.4802432   1.5197568  98.4802432  0.0000000 100.0000000
Bukkuyum            94.4444444   5.5555556  94.4444444  0.0000000  93.3333333
Bungudu             74.2857143  25.7142857  74.2857143  0.0000000  74.2857143
Bunkure             90.5511811   9.4488189  90.5511811  0.0000000 100.0000000
Bunza               68.7116564  31.2883436  68.7116564  0.0000000  95.7055215
Bursari             38.1443299  61.8556701  38.1443299  0.0000000 100.0000000
Buruku              86.3636364  13.6363636  86.3636364  0.0000000 100.0000000
Burutu              57.1428571  42.8571429  57.1428571  0.0000000 100.0000000
Bwari               55.0847458  44.9152542  55.0847458  0.0000000  61.8644068
Calabar-Municipal   53.6585366  46.3414634  53.6585366  0.0000000  12.1951220
Calabar South       57.5342466  42.4657534  57.5342466  0.0000000  12.3287671
Chanchaga           95.8333333   4.1666667  95.8333333  0.0000000   1.3888889
Charanchi           87.8172589  12.1827411  87.8172589  0.0000000  81.7258883
Chibok              63.6363636  36.3636364  63.6363636  0.0000000  66.6666667
Chikun             100.0000000   0.0000000 100.0000000  0.0000000 100.0000000
Dala                57.2463768  42.7536232  57.2463768  0.0000000   0.0000000
Damaturu             0.0000000 100.0000000   0.0000000  0.0000000  35.8974359
Damban              85.1351351  14.8648649  85.1351351  0.0000000  70.2702703
Dambatta            82.2660099  17.7339901  82.2660099  0.0000000  84.2364532
Damboa             100.0000000   0.0000000 100.0000000  0.0000000  11.1111111
Dan Musa            76.5625000  23.4375000  76.5625000  0.0000000 100.0000000
Dandi               72.7272727  27.2727273  72.7272727  0.0000000  93.3333333
Dandume             76.5217391  23.4782609  76.5217391  0.0000000  73.0434783
Dange-Shuni         36.3636364  63.6363636  36.3636364  0.0000000  93.5064935
Danja               62.9629630  37.0370370  62.9629630  0.0000000 100.0000000
Darazo              92.2222222   7.7777778  92.2222222  0.0000000  85.5555556
Dass                95.6521739   4.3478261  95.6521739  0.0000000  89.2976589
Daura               63.1578947  36.8421053  63.1578947  0.0000000  47.3684211
Dawakin Kudu        90.5213270   9.4786730  90.5213270  0.0000000  81.5165877
Dawakin Tofa        98.6013986   1.3986014  98.6013986  0.0000000  82.5174825
Degema              25.0000000  75.0000000  25.0000000  0.0000000  75.0000000
Dekina              10.5769231  89.4230769  10.5769231  0.0000000  94.2307692
Demsa               92.3076923   7.6923077  92.3076923  0.0000000 100.0000000
Dikwa               95.0000000   5.0000000  95.0000000  0.0000000   7.5000000
Doguwa              94.5454545   5.4545455  94.5454545  0.0000000 100.0000000
Doma                25.0000000  75.0000000  25.0000000  0.0000000  44.3181818
Donga               87.5675676  12.4324324  87.5675676  0.0000000  84.8648649
Dukku               77.2058824  22.7941176  77.2058824  0.0000000 100.0000000
Dunukofia           15.5555556  84.4444444  15.5555556  0.0000000  24.4444444
Dutse               93.5064935   6.4935065  93.5064935  0.0000000  88.9610390
Dutsi               72.5000000  27.5000000  72.5000000  0.0000000 100.0000000
Dutsin-Ma           88.0258900  11.9741100  88.0258900  0.0000000  86.7313916
Eastern Obolo       24.0000000  76.0000000  24.0000000  0.0000000 100.0000000
Ebonyi              89.2307692   5.7692308  94.2307692  5.0000000  93.0769231
Edati               53.7234043  46.2765957  53.7234043  0.0000000 100.0000000
Ede North           63.8888889  36.1111111  63.8888889  0.0000000  29.6296296
Ede South           89.0410959  10.9589041  89.0410959  0.0000000  93.1506849
Edu                 68.7500000  31.2500000  68.7500000  0.0000000  79.6875000
Efon                55.5555556  26.6666667  73.3333333 17.7777778  64.4444444
Egbado North        53.3333333  46.6666667  53.3333333  0.0000000  76.6666667
Egbado South        15.6250000  84.3750000  15.6250000  0.0000000  43.7500000
Egbeda              81.4432990  18.5567010  81.4432990  0.0000000  51.5463918
Egbedore            79.8742138  20.1257862  79.8742138  0.0000000  72.3270440
Egor                 0.0000000 100.0000000   0.0000000  0.0000000   0.0000000
Ehime-Mbano         10.6382979  89.3617021  10.6382979  0.0000000  42.5531915
Ejigbo              92.1810700   7.8189300  92.1810700  0.0000000  61.9341564
Ekeremor            18.1818182  81.8181818  18.1818182  0.0000000  90.9090909
Eket                 8.8235294  91.1764706   8.8235294  0.0000000  80.8823529
Ekiti               64.8514851  35.1485149  64.8514851  0.0000000 100.0000000
Ekiti East          60.0000000  13.3333333  86.6666667 26.6666667  52.2222222
Ekiti South West    54.9382716  38.2716049  61.7283951  6.7901235  45.0617284
Ekiti West          63.2478632  29.0598291  70.9401709  7.6923077  77.7777778
Ekwusigo            86.1111111  13.8888889  86.1111111  0.0000000  19.4444444
Eleme               50.0000000  50.0000000  50.0000000  0.0000000  50.0000000
Emohua              83.3333333  16.6666667  83.3333333  0.0000000 100.0000000
Emure               55.3846154  30.7692308  69.2307692 13.8461538  58.4615385
Enugu East          56.5217391  43.4782609  56.5217391  0.0000000  21.7391304
Enugu North         83.3333333  16.6666667  83.3333333  0.0000000   4.1666667
Enugu South         57.8947368  42.1052632  57.8947368  0.0000000  26.3157895
Epe                 28.9855072  70.0483092  29.9516908  0.9661836  73.9130435
Esan Central        21.8750000  78.1250000  21.8750000  0.0000000  81.2500000
Esan North East      5.8823529  94.1176471   5.8823529  0.0000000  58.8235294
Esan South East      7.1428571  92.8571429   7.1428571  0.0000000 100.0000000
Esan West           20.5882353  79.4117647  20.5882353  0.0000000  70.5882353
Ese-Odo             18.9189189  81.0810811  18.9189189  0.0000000  99.0990991
Esit - Eket         28.9473684  71.0526316  28.9473684  0.0000000 100.0000000
Essien Udim          3.4482759  96.5517241   3.4482759  0.0000000  91.3793103
Etche               36.8421053  63.1578947  36.8421053  0.0000000 100.0000000
Ethiope East        13.6363636  86.3636364  13.6363636  0.0000000  95.4545455
Ethiope West        20.0000000  80.0000000  20.0000000  0.0000000  84.0000000
Eti-Osa             21.0526316  78.9473684  21.0526316  0.0000000  10.5263158
Etim Ekpo            9.8039216  90.1960784   9.8039216  0.0000000 100.0000000
Etinan              20.0000000  80.0000000  20.0000000  0.0000000  91.4285714
Etsako Central      25.0000000  75.0000000  25.0000000  0.0000000 100.0000000
Etsako East          9.5238095  90.4761905   9.5238095  0.0000000  88.0952381
Etsako West          0.0000000 100.0000000   0.0000000  0.0000000  40.9090909
Etung               62.4242424  37.5757576  62.4242424  0.0000000 100.0000000
Ewekoro             56.9444444  43.0555556  56.9444444  0.0000000  98.6111111
Ezeagu              64.7058824  35.2941176  64.7058824  0.0000000 100.0000000
Ezinihitte          39.2857143  60.7142857  39.2857143  0.0000000  53.5714286
Ezza North          95.3296703   2.4725275  97.5274725  2.1978022  99.1758242
Ezza South          91.4012739   4.7770701  95.2229299  3.8216561  94.2675159
Fagge               84.1269841  15.8730159  84.1269841  0.0000000   7.9365079
Fakai               85.4545455  14.5454545  85.4545455  0.0000000  80.0000000
Faskari             85.7971014  14.2028986  85.7971014  0.0000000 100.0000000
Fika                 0.0000000 100.0000000   0.0000000  0.0000000  94.7368421
Fufore              46.1538462  53.8461538  46.1538462  0.0000000 100.0000000
Funakaye            73.6842105  26.3157895  73.6842105  0.0000000  67.3684211
Fune                 1.4084507  98.5915493   1.4084507  0.0000000  70.4225352
Funtua              62.8571429  37.1428571  62.8571429  0.0000000  50.0000000
Gabasawa            84.3137255  15.6862745  84.3137255  0.0000000 100.0000000
Gada                66.1016949  33.8983051  66.1016949  0.0000000 100.0000000
Gagarawa            99.1011236   0.8988764  99.1011236  0.0000000 100.0000000
Gamawa              95.0338600   4.9661400  95.0338600  0.0000000  88.2618510
Ganjuwa             95.0617284   4.9382716  95.0617284  0.0000000  91.3580247
Ganye               71.4285714  28.5714286  71.4285714  0.0000000  64.2857143
Garki               97.2286374   2.7713626  97.2286374  0.0000000 100.0000000
Garko               85.3932584  14.6067416  85.3932584  0.0000000  66.2921348
Garum Mallam        92.6380368   7.3619632  92.6380368  0.0000000 100.0000000
Gashaka             90.9090909   9.0909091  90.9090909  0.0000000 100.0000000
Gassol              85.6603774  14.3396226  85.6603774  0.0000000  86.0377358
Gaya               100.0000000   0.0000000 100.0000000  0.0000000  82.4742268
Gbako               54.8571429  45.1428571  54.8571429  0.0000000 100.0000000
Gboko               73.1958763  26.8041237  73.1958763  0.0000000  80.4123711
Geidam               0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Gezawa              89.3992933  10.6007067  89.3992933  0.0000000  95.0530035
Giade               96.3636364   3.6363636  96.3636364  0.0000000  97.2727273
Girei               60.0000000  40.0000000  60.0000000  0.0000000  93.3333333
Giwa                94.5736434   5.4263566  94.5736434  0.0000000  68.2170543
Gokana               0.0000000 100.0000000   0.0000000  0.0000000  50.0000000
Gombe               48.5714286  51.4285714  48.5714286  0.0000000   0.0000000
Gombi               77.7777778  22.2222222  77.7777778  0.0000000  80.0000000
Goronyo             57.1428571  42.8571429  57.1428571  0.0000000  92.8571429
Gubio                0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Gudu                47.5000000  52.5000000  47.5000000  0.0000000 100.0000000
Gujba                0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Gulani               0.0000000 100.0000000   0.0000000  0.0000000 100.0000000
Guma                85.4700855  14.5299145  85.4700855  0.0000000 100.0000000
Gumel               62.4020888  37.5979112  62.4020888  0.0000000  64.4908616
Gummi               92.5373134   7.4626866  92.5373134  0.0000000  58.2089552
Gurara              87.8048780  12.1951220  87.8048780  0.0000000  97.5609756
Guri                98.8338192   1.1661808  98.8338192  0.0000000 100.0000000
Gusau               87.5000000  12.5000000  87.5000000  0.0000000  78.1250000
Guyuk               59.0909091  40.9090909  59.0909091  0.0000000  77.2727273
Guzamala             0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Gwadabawa           69.2982456  30.7017544  69.2982456  0.0000000  90.3508772
Gwagwalada          61.8644068  38.1355932  61.8644068  0.0000000  88.9830508
Gwale               85.4838710  14.5161290  85.4838710  0.0000000   0.0000000
Gwandu              80.6122449  19.3877551  80.6122449  0.0000000 100.0000000
Gwaram              91.3461538   8.6538462  91.3461538  0.0000000  97.1153846
Gwarzo              86.8421053  11.8421053  88.1578947  1.3157895  64.4736842
Gwer East           84.1269841  15.8730159  84.1269841  0.0000000  92.0634921
Gwer West           80.9523810  19.0476190  80.9523810  0.0000000  90.4761905
Gwiwa               98.0198020   1.9801980  98.0198020  0.0000000 100.0000000
Gwoza               65.9090909  34.0909091  65.9090909  0.0000000  52.2727273
Hadejia             93.7500000   6.2500000  93.7500000  0.0000000   5.2083333
Hawul               65.6250000  34.3750000  65.6250000  0.0000000 100.0000000
Hong                75.0000000  25.0000000  75.0000000  0.0000000 100.0000000
Ibadan North        39.3939394  60.6060606  39.3939394  0.0000000   0.0000000
Ibadan North East   37.7952756  62.2047244  37.7952756  0.0000000   0.0000000
Ibadan North West   11.4942529  88.5057471  11.4942529  0.0000000   0.0000000
Ibadan South East   41.6666667  55.5555556  44.4444444  2.7777778   0.0000000
Ibadan South West   31.7757009  68.2242991  31.7757009  0.0000000   0.9345794
Ibaji               38.2352941  61.7647059  38.2352941  0.0000000 100.0000000
Ibarapa Central     78.4688995  21.5311005  78.4688995  0.0000000  44.0191388
Ibarapa East        71.5686275  28.4313725  71.5686275  0.0000000  61.7647059
Ibarapa North       75.2525253  24.7474747  75.2525253  0.0000000  61.6161616
Ibeju/Lekki         21.0526316  78.9473684  21.0526316  0.0000000  87.2180451
Ibeno               25.0000000  75.0000000  25.0000000  0.0000000  90.0000000
Ibesikpo Asutan      1.8518519  98.1481481   1.8518519  0.0000000  92.5925926
Ibi                 91.4634146   8.5365854  91.4634146  0.0000000 100.0000000
Ibiono Ibom          7.8431373  92.1568627   7.8431373  0.0000000  88.2352941
Idah                49.1525424  50.8474576  49.1525424  0.0000000  25.4237288
Idanre              87.5000000  12.5000000  87.5000000  0.0000000  90.3846154
Ideato North        64.2857143  35.7142857  64.2857143  0.0000000  71.4285714
Ideato South        43.4782609  56.5217391  43.4782609  0.0000000  56.5217391
Idemili North       87.7551020  12.2448980  87.7551020  0.0000000   4.0816327
Idemili South       83.9285714  16.0714286  83.9285714  0.0000000   0.0000000
Ido                 51.4563107  48.5436893  51.4563107  0.0000000  83.4951456
Ido-Osi             50.0000000  18.7500000  81.2500000 31.2500000  70.8333333
Ifako-Ijaye         18.0327869  81.9672131  18.0327869  0.0000000   0.0000000
Ife Central         55.1948052  44.8051948  55.1948052  0.0000000  18.1818182
Ife East            54.3147208  45.6852792  54.3147208  0.0000000  21.8274112
Ife North           60.5839416  39.4160584  60.5839416  0.0000000 100.0000000
Ife South           74.7191011  25.2808989  74.7191011  0.0000000  90.4494382
Ifedayo             71.4285714  28.5714286  71.4285714  0.0000000 100.0000000
Ifedore             74.1935484  25.8064516  74.1935484  0.0000000  61.9354839
Ifelodun Kwara      69.8835275  30.1164725  69.8835275  0.0000000  89.3510815
Ifelodun Osun       64.2857143  35.7142857  64.2857143  0.0000000  30.1587302
Ifo                 78.3132530  21.6867470  78.3132530  0.0000000  27.7108434
Igabi               93.1740614   6.8259386  93.1740614  0.0000000  87.3720137
Igalamela-Odolu     25.0000000  75.0000000  25.0000000  0.0000000 100.0000000
Igbo-Etiti          52.2727273  45.4545455  54.5454545  2.2727273  65.9090909
Igbo-Eze North      45.2380952  54.7619048  45.2380952  0.0000000  90.4761905
Igbo-Eze South      52.9411765  47.0588235  52.9411765  0.0000000  85.2941176
Igueben              0.0000000 100.0000000   0.0000000  0.0000000 100.0000000
Ihiala             100.0000000   0.0000000 100.0000000  0.0000000  13.7404580
Ihitte/Uboma        33.3333333  58.3333333  41.6666667  8.3333333  55.5555556
Ijebu East          80.6451613  19.3548387  80.6451613  0.0000000 100.0000000
Ijebu North         45.3125000  48.4375000  51.5625000  6.2500000  48.4375000
Ijebu North East    28.8888889  71.1111111  28.8888889  0.0000000  68.8888889
Ijebu Ode           25.8620690  74.1379310  25.8620690  0.0000000  25.8620690
Ijero               58.7378641  24.2718447  75.7281553 16.9902913  70.8737864
Ijumu               50.0000000  50.0000000  50.0000000  0.0000000  84.4262295
Ika                 14.2857143  85.7142857  14.2857143  0.0000000 100.0000000
Ika North East       0.0000000 100.0000000   0.0000000  0.0000000 100.0000000
Ika South            0.0000000 100.0000000   0.0000000  0.0000000 100.0000000
Ikara               86.8613139  13.1386861  86.8613139  0.0000000  82.4817518
Ikeduru             53.3333333  46.6666667  53.3333333  0.0000000  84.4444444
Ikeja               57.1428571  42.8571429  57.1428571  0.0000000   0.0000000
Ikenne              52.6315789  47.3684211  52.6315789  0.0000000  52.6315789
Ikere               69.0721649  26.8041237  73.1958763  4.1237113   7.2164948
Ikole               60.0000000  17.2972973  82.7027027 22.7027027  64.8648649
Ikom                66.6666667  33.3333333  66.6666667  0.0000000  88.5572139
Ikono                6.3492063  93.6507937   6.3492063  0.0000000 100.0000000
Ikorodu             21.2500000  70.0000000  30.0000000  8.7500000  46.2500000
Ikot Abasi          15.5172414  84.4827586  15.5172414  0.0000000  89.6551724
Ikot Ekpene          2.1276596  97.8723404   2.1276596  0.0000000  89.3617021
Ikpoba-Okha          1.5151515  98.4848485   1.5151515  0.0000000  24.2424242
Ikwerre             45.4545455  54.5454545  45.4545455  0.0000000  78.7878788
Ikwo                89.3081761   3.1446541  96.8553459  7.5471698  98.4276730
Ikwuano             52.3364486  47.6635514  52.3364486  0.0000000  82.2429907
Ila                 65.3179191  34.1040462  65.8959538  0.5780347  47.9768786
Ilaje                0.0000000 100.0000000   0.0000000  0.0000000  79.3103448
Ile-Oluji-Okeigbo   57.5539568  42.4460432  57.5539568  0.0000000  62.5899281
Ilejemeji           59.5238095  26.1904762  73.8095238 14.2857143 100.0000000
Ilesha East         47.4820144  52.5179856  47.4820144  0.0000000   7.9136691
Ilesha West         57.7319588  42.2680412  57.7319588  0.0000000   1.0309278
Illela              47.7272727  52.2727273  47.7272727  0.0000000  87.8787879
Ilorin East         64.8648649  35.1351351  64.8648649  0.0000000  45.0450450
Ilorin South        66.6666667  33.3333333  66.6666667  0.0000000  27.4509804
Ilorin West         53.5947712  46.4052288  53.5947712  0.0000000   2.6143791
Imeko-Afon          87.8787879  12.1212121  87.8787879  0.0000000  78.7878788
Ingawa              92.9292929   7.0707071  92.9292929  0.0000000 100.0000000
Ini                 21.7391304  78.2608696  21.7391304  0.0000000  95.6521739
Ipokia              15.8730159  71.4285714  28.5714286 12.6984127  95.2380952
Irele               14.9425287  85.0574713  14.9425287  0.0000000  63.2183908
Irepo               61.8644068  38.1355932  61.8644068  0.0000000  24.5762712
Irepodun Kwara      62.3076923  37.6923077  62.3076923  0.0000000  88.8461538
Irepodun Osun       45.1923077  54.8076923  45.1923077  0.0000000   9.6153846
Irepodun/Ifelodun   42.4778761  25.6637168  74.3362832 31.8584071  99.1150442
Irewole             55.8558559  44.1441441  55.8558559  0.0000000  45.9459459
Isa                 60.3773585  39.6226415  60.3773585  0.0000000  58.4905660
Ise/Orun            68.5714286  22.8571429  77.1428571  8.5714286  65.7142857
Iseyin              72.6415094  27.3584906  72.6415094  0.0000000  66.9811321
Ishielu             88.1147541   6.1475410  93.8524590  5.7377049  99.1803279
Isi-Uzo             87.9310345  12.0689655  87.9310345  0.0000000  87.9310345
Isiala-Ngwa North   58.4269663  41.5730337  58.4269663  0.0000000  93.2584270
Isiala-Ngwa South   26.5625000  73.4375000  26.5625000  0.0000000  89.0625000
Isiala Mbano        13.1578947  86.8421053  13.1578947  0.0000000  55.2631579
Isin                59.3548387  40.0000000  60.0000000  0.6451613 100.0000000
Isiukwuato          40.9836066  59.0163934  40.9836066  0.0000000 100.0000000
Isokan              73.9837398  26.0162602  73.9837398  0.0000000  58.5365854
Isoko North          7.6923077  92.3076923   7.6923077  0.0000000  71.7948718
Isoko South         12.5000000  87.5000000  12.5000000  0.0000000  84.3750000
Isu                 62.5000000  37.5000000  62.5000000  0.0000000  54.1666667
Itas/Gadau          87.4125874  12.5874126  87.4125874  0.0000000 100.0000000
Itesiwaju           53.5211268  46.4788732  53.5211268  0.0000000  78.8732394
Itu                  6.6666667  93.3333333   6.6666667  0.0000000  95.0000000
Ivo                 79.5454545  15.9090909  84.0909091  4.5454545  72.7272727
Iwajowa             71.3725490  28.6274510  71.3725490  0.0000000 100.0000000
Iwo                 55.0000000  45.0000000  55.0000000  0.0000000  25.6250000
Izzi                94.9843260   3.1347962  96.8652038  1.8808777  99.3730408
Jaba                96.7320261   3.2679739  96.7320261  0.0000000  84.3137255
Jada                57.1428571  42.8571429  57.1428571  0.0000000  71.4285714
Jahun               95.8333333   4.1666667  95.8333333  0.0000000  90.5303030
Jakusko             20.0000000  80.0000000  20.0000000  0.0000000  74.2857143
Jalingo             87.6811594  12.3188406  87.6811594  0.0000000  27.5362319
Jama'are            91.2698413   8.7301587  91.2698413  0.0000000  75.3968254
Jega                39.8648649  60.1351351  39.8648649  0.0000000  57.4324324
Jema'a              91.8088737   8.1911263  91.8088737  0.0000000  81.2286689
Jere                44.0789474  55.9210526  44.0789474  0.0000000  38.8157895
Jibia               83.1578947  16.8421053  83.1578947  0.0000000  72.6315789
Jos East            89.0547264  10.9452736  89.0547264  0.0000000 100.0000000
Jos North           64.6153846  35.3846154  64.6153846  0.0000000  26.1538462
Jos South           89.8395722  10.1604278  89.8395722  0.0000000  57.2192513
Kabba/Bunu          63.0136986  36.9863014  63.0136986  0.0000000  84.9315068
Kabo               100.0000000   0.0000000 100.0000000  0.0000000 100.0000000
Kachia              96.1538462   3.8461538  96.1538462  0.0000000 100.0000000
Kaduna North        77.5862069  22.4137931  77.5862069  0.0000000   0.0000000
Kaduna South        93.2432432   6.7567568  93.2432432  0.0000000   0.0000000
Kafin Hausa        100.0000000   0.0000000 100.0000000  0.0000000 100.0000000
Kafur               83.7398374  16.2601626  83.7398374  0.0000000 100.0000000
Kaga                 0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Kagarko             96.2162162   3.7837838  96.2162162  0.0000000  91.3513514
Kaiama              90.2912621   9.7087379  90.2912621  0.0000000  59.2233010
Kaita               80.0664452  19.9335548  80.0664452  0.0000000 100.0000000
Kajola              71.3483146  28.6516854  71.3483146  0.0000000  29.2134831
Kajuru              95.1048951   4.8951049  95.1048951  0.0000000 100.0000000
Kala/Balge           0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Kalgo               60.0000000  40.0000000  60.0000000  0.0000000 100.0000000
Kaltungo            82.0000000  18.0000000  82.0000000  0.0000000  68.3333333
Kanam               93.1034483   6.8965517  93.1034483  0.0000000  89.3103448
Kankara             68.5039370  31.4960630  68.5039370  0.0000000  79.5275591
Kanke               93.3035714   6.6964286  93.3035714  0.0000000 100.0000000
Kankia              82.0143885  17.9856115  82.0143885  0.0000000  76.9784173
Kano Municipal      69.3965517  30.6034483  69.3965517  0.0000000   0.0000000
Karasuwa            66.1764706  33.8235294  66.1764706  0.0000000 100.0000000
Karaye             100.0000000   0.0000000 100.0000000  0.0000000  98.2300885
Karim-Lamido        80.8641975  19.1358025  80.8641975  0.0000000 100.0000000
Karu                41.5730337  58.4269663  41.5730337  0.0000000  80.8988764
Katagum             72.8813559  27.1186441  72.8813559  0.0000000  83.8983051
Katcha              89.1304348  10.8695652  89.1304348  0.0000000 100.0000000
Katsina             57.9545455  42.0454545  57.9545455  0.0000000   9.0909091
Katsina-Ala         89.2405063  10.7594937  89.2405063  0.0000000  92.4050633
Kaugama             99.4555354   0.5444646  99.4555354  0.0000000 100.0000000
Kaura               91.4893617   8.5106383  91.4893617  0.0000000  82.2695035
Kaura Namoda        89.4736842  10.5263158  89.4736842  0.0000000  86.3157895
Kauru               98.2658960   1.7341040  98.2658960  0.0000000 100.0000000
Kazaure             56.3968668  43.6031332  56.3968668  0.0000000  51.9582245
Keana               63.6363636  36.3636364  63.6363636  0.0000000  83.1168831
Kebbe               65.6250000  34.3750000  65.6250000  0.0000000 100.0000000
Keffi               22.0779221  77.9220779  22.0779221  0.0000000  11.6883117
Khana                6.6666667  93.3333333   6.6666667  0.0000000  93.3333333
Kibiya              95.7746479   3.7558685  96.2441315  0.4694836 100.0000000
Kirfi               80.4597701  18.3908046  81.6091954  1.1494253 100.0000000
Kiri Kasamma        55.7114228  44.2885772  55.7114228  0.0000000 100.0000000
Kiru                91.1894273   8.8105727  91.1894273  0.0000000  85.9030837
Kiyawa              98.3695652   1.6304348  98.3695652  0.0000000  91.8478261
Kogi                54.2857143  45.7142857  54.2857143  0.0000000 100.0000000
Koko/Besse          80.4054054  19.5945946  80.4054054  0.0000000  62.1621622
Kokona              68.5714286  31.4285714  68.5714286  0.0000000 100.0000000
Kolokuma/Opokuma     5.0000000  95.0000000   5.0000000  0.0000000 100.0000000
Konduga             66.6666667  33.3333333  66.6666667  0.0000000 100.0000000
Konshisha           95.0138504   4.9861496  95.0138504  0.0000000  97.5069252
Kontagora           95.0617284   4.9382716  95.0617284  0.0000000   9.8765432
Kosofe              43.1034483  56.8965517  43.1034483  0.0000000   0.0000000
Kubau               95.8333333   4.1666667  95.8333333  0.0000000  91.2500000
Kudan              100.0000000   0.0000000 100.0000000  0.0000000 100.0000000
Kuje                37.0629371  62.9370629  37.0629371  0.0000000  90.2097902
Kukawa               0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Kumbotso            92.1568627   7.8431373  92.1568627  0.0000000  49.0196078
Kunchi              97.6562500   2.3437500  97.6562500  0.0000000 100.0000000
Kura                89.3129771  10.6870229  89.3129771  0.0000000  49.6183206
Kurfi               86.4077670  13.5922330  86.4077670  0.0000000 100.0000000
Kurmi               92.8571429   7.1428571  92.8571429  0.0000000 100.0000000
Kusada              88.2352941  11.7647059  88.2352941  0.0000000 100.0000000
Kwali               56.1797753  43.8202247  56.1797753  0.0000000  90.4494382
Kwami               77.3333333  22.6666667  77.3333333  0.0000000  98.6666667
Kwande              86.2068966  13.7931034  86.2068966  0.0000000  98.8505747
Kware               48.0916031  51.9083969  48.0916031  0.0000000  95.4198473
Kwaya Kusar        100.0000000   0.0000000 100.0000000  0.0000000 100.0000000
Lafia               34.6863469  64.9446494  35.0553506  0.3690037  54.9815498
Lagelu              60.8000000  39.2000000  60.8000000  0.0000000  83.2000000
Lagos Island        13.5135135  86.4864865  13.5135135  0.0000000   0.0000000
Lagos Mainland      30.9523810  69.0476190  30.9523810  0.0000000   0.0000000
Lamurde            100.0000000   0.0000000 100.0000000  0.0000000 100.0000000
Langtang North      93.8666667   6.1333333  93.8666667  0.0000000  88.0000000
Langtang South      81.5217391  18.4782609  81.5217391  0.0000000 100.0000000
Lapai               62.7118644  37.2881356  62.7118644  0.0000000  66.1016949
Lau                 89.2045455  10.7954545  89.2045455  0.0000000 100.0000000
Lavun               45.0000000  55.0000000  45.0000000  0.0000000 100.0000000
Lere                98.3695652   1.6304348  98.3695652  0.0000000  98.9130435
Logo                87.0748299  12.9251701  87.0748299  0.0000000  94.5578231
Lokoja              52.6315789  47.3684211  52.6315789  0.0000000  47.3684211
Machina             72.9729730  27.0270270  72.9729730  0.0000000 100.0000000
Madagali             0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Madobi              98.8571429   1.1428571  98.8571429  0.0000000  96.5714286
Mafa                 0.0000000 100.0000000   0.0000000  0.0000000 100.0000000
Magama              97.0000000   3.0000000  97.0000000  0.0000000  97.0000000
Magumeri            77.7777778  22.2222222  77.7777778  0.0000000  77.7777778
Mai'adua            62.5498008  37.4501992  62.5498008  0.0000000  76.8924303
Maiduguri           17.2932331  82.7067669  17.2932331  0.0000000   3.0075188
Maigatari           93.5294118   6.4705882  93.5294118  0.0000000  79.4117647
Maiha               82.3529412  17.6470588  82.3529412  0.0000000  88.2352941
Maiyama             66.0000000  34.0000000  66.0000000  0.0000000 100.0000000
Makoda              19.8113208  80.1886792  19.8113208  0.0000000 100.0000000
Makurdi             66.3043478  33.6956522  66.3043478  0.0000000  22.8260870
Malam Madori        98.4126984   1.5873016  98.4126984  0.0000000  89.2857143
Malumfashi          87.5000000  12.5000000  87.5000000  0.0000000  75.0000000
Mangu               87.9746835  12.0253165  87.9746835  0.0000000  90.1898734
Mani                82.0689655  15.1724138  84.8275862  2.7586207 100.0000000
Maradun             79.1666667  20.8333333  79.1666667  0.0000000 100.0000000
Mariga              88.1355932  11.8644068  88.1355932  0.0000000 100.0000000
Markafi             91.2280702   8.7719298  91.2280702  0.0000000  80.7017544
Marte                0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Maru                75.4491018  24.5508982  75.4491018  0.0000000 100.0000000
Mashegu             76.7857143  23.2142857  76.7857143  0.0000000 100.0000000
Mashi               63.1147541  36.8852459  63.1147541  0.0000000  73.7704918
Matazu              84.9740933  14.5077720  85.4922280  0.5181347 100.0000000
Mayo-Belwa          91.6666667   8.3333333  91.6666667  0.0000000 100.0000000
Mbaitoli            52.7272727  47.2727273  52.7272727  0.0000000  90.9090909
Mbo                 16.6666667  83.3333333  16.6666667  0.0000000 100.0000000
Michika             80.0000000  20.0000000  80.0000000  0.0000000  85.0000000
Miga                95.3125000   4.6875000  95.3125000  0.0000000 100.0000000
Mikang              91.1864407   8.8135593  91.1864407  0.0000000 100.0000000
Minjibir            90.2912621   9.7087379  90.2912621  0.0000000 100.0000000
Misau               93.8931298   6.1068702  93.8931298  0.0000000  62.5954198
Mkpat Enin          28.6956522  71.3043478  28.6956522  0.0000000  97.3913043
Moba                46.0526316  21.0526316  78.9473684 32.8947368  69.7368421
Mobbar               0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Mokwa               42.1052632  57.8947368  42.1052632  0.0000000  75.7894737
Monguno             96.1538462   3.8461538  96.1538462  0.0000000  42.3076923
Mopa-Muro           66.2790698  33.7209302  66.2790698  0.0000000 100.0000000
Moro                86.2857143  13.7142857  86.2857143  0.0000000 100.0000000
Mubi North           0.0000000 100.0000000   0.0000000  0.0000000   0.0000000
Mubi South           0.0000000 100.0000000   0.0000000  0.0000000   0.0000000
Musawa              91.6083916   8.3916084  91.6083916  0.0000000 100.0000000
Mushin               9.3750000  90.6250000   9.3750000  0.0000000   0.0000000
Muya                93.6842105   6.3157895  93.6842105  0.0000000 100.0000000
Nafada              62.8378378  37.1621622  62.8378378  0.0000000 100.0000000
Nangere              8.1081081  91.8918919   8.1081081  0.0000000 100.0000000
Nasarawa Kano       48.0000000  52.0000000  48.0000000  0.0000000   0.0000000
Nasarawa            58.6206897  41.3793103  58.6206897  0.0000000  77.2413793
Nasarawa-Eggon      52.8301887  47.1698113  52.8301887  0.0000000  88.6792453
Ndokwa East         32.5301205  53.0120482  46.9879518 14.4578313 100.0000000
Ndokwa West         15.2173913  84.7826087  15.2173913  0.0000000  95.6521739
Nembe               23.2558140  74.4186047  25.5813953  2.3255814  81.3953488
Ngala               93.0000000   7.0000000  93.0000000  0.0000000  16.0000000
Nganzai              0.0000000   0.0000000   0.0000000  0.0000000   0.0000000
Ngaski              93.9130435   6.0869565  93.9130435  0.0000000 100.0000000
Ngor-Okpala         51.4705882  48.5294118  51.4705882  0.0000000  89.7058824
Nguru               88.0630631  11.9369369  88.0630631  0.0000000  54.9549550
Ningi               96.7391304   3.2608696  96.7391304  0.0000000  77.1739130
Njaba               46.6666667  53.3333333  46.6666667  0.0000000  26.6666667
Njikoka             70.5882353  29.4117647  70.5882353  0.0000000  26.4705882
Nkanu East          96.2962963   1.8518519  98.1481481  1.8518519 100.0000000
Nkanu West          77.2151899  21.5189873  78.4810127  1.2658228  72.1518987
Nkwerre             12.1212121  87.8787879  12.1212121  0.0000000  24.2424242
Nnewi North         46.1538462  53.8461538  46.1538462  0.0000000   0.0000000
Nnewi South         93.9393939   6.0606061  93.9393939  0.0000000  51.5151515
Nsit Atai           13.2352941  86.7647059  13.2352941  0.0000000 100.0000000
Nsit Ibom            8.1632653  91.8367347   8.1632653  0.0000000 100.0000000
Nsit Ubium           9.0909091  90.9090909   9.0909091  0.0000000 100.0000000
Nsukka              60.7142857  39.2857143  60.7142857  0.0000000  64.2857143
Numan                0.0000000 100.0000000   0.0000000  0.0000000   0.0000000
Nwangele            28.5714286  71.4285714  28.5714286  0.0000000   0.0000000
Obafemi-Owode       48.4848485  51.5151515  48.4848485  0.0000000  89.3939394
Obanliku            70.3296703  29.6703297  70.3296703  0.0000000  98.3516484
Nasarawa Nasarawa   92.1686747   7.8313253  92.1686747  0.0000000  94.5783133
Obi Nasarawa        53.4090909  46.5909091  53.4090909  0.0000000 100.0000000
Obi Ngwa            43.9306358  56.0693642  43.9306358  0.0000000  98.2658960
Obia/Akpor          27.2000000  72.8000000  27.2000000  0.0000000  67.2000000
Obokun              55.4455446  44.5544554  55.4455446  0.0000000  99.0099010
Obot Akara          11.1111111  88.8888889  11.1111111  0.0000000 100.0000000
Obowo               40.9090909  59.0909091  40.9090909  0.0000000  51.5151515
Obubra              72.5321888  26.1802575  73.8197425  1.2875536  86.2660944
Obudu               66.9456067  33.0543933  66.9456067  0.0000000  89.9581590
Odeda               69.0265487  30.9734513  69.0265487  0.0000000  80.5309735
Odigbo              50.0000000  50.0000000  50.0000000  0.0000000  95.3488372
Odo-Otin            66.4473684  33.5526316  66.4473684  0.0000000  87.1710526
Odogbolu            25.9740260  72.7272727  27.2727273  1.2987013  98.7012987
Odukpani            34.7305389  65.2694611  34.7305389  0.0000000 100.0000000
Offa                60.6382979  39.3617021  60.6382979  0.0000000  18.0851064
Ofu                 20.5882353  79.4117647  20.5882353  0.0000000 100.0000000
Ogba/Egbema/Ndoni   30.6451613  69.3548387  30.6451613  0.0000000  61.2903226
Ogbadibo            39.5061728  60.4938272  39.5061728  0.0000000  93.8271605
Ogbaru              54.3478261  45.6521739  54.3478261  0.0000000  28.2608696
Ogbia               87.5000000  12.5000000  87.5000000  0.0000000 100.0000000
Ogbomosho North     12.0000000  88.0000000  12.0000000  0.0000000   4.0000000
Ogbomosho South     35.3448276  64.6551724  35.3448276  0.0000000  14.6551724
Ogo Oluwa           46.2365591  53.7634409  46.2365591  0.0000000  93.0107527
Ogoja               79.8534799  20.1465201  79.8534799  0.0000000  89.3772894
Ogori/Magongo       27.0833333  70.8333333  29.1666667  2.0833333 100.0000000
Ogu/Bolo             0.0000000 100.0000000   0.0000000  0.0000000 100.0000000
Ogun waterside      13.5135135  86.4864865  13.5135135  0.0000000 100.0000000
Oguta               50.5050505  49.4949495  50.5050505  0.0000000  93.9393939
Ohafia              36.8421053  63.1578947  36.8421053  0.0000000  57.8947368
Ohaji/Egbema        81.4606742  17.9775281  82.0224719  0.5617978 100.0000000
Ohaozara            85.8181818  11.2727273  88.7272727  2.9090909  77.0909091
Ohaukwu             91.3165266   3.6414566  96.3585434  5.0420168 100.0000000
Ohimini             90.7692308   9.2307692  90.7692308  0.0000000 100.0000000
Oji-River           55.2631579  44.7368421  55.2631579  0.0000000 100.0000000
Ojo                 49.4252874  50.5747126  49.4252874  0.0000000  19.5402299
Oju                 94.5054945   5.4945055  94.5054945  0.0000000  95.9706960
Oke-Ero             73.9130435  25.6038647  74.3961353  0.4830918 100.0000000
Okehi               60.8108108  39.1891892  60.8108108  0.0000000  32.4324324
Okene               20.0000000  80.0000000  20.0000000  0.0000000   0.0000000
Okigwe              12.1951220  87.8048780  12.1951220  0.0000000  68.2926829
Okitipupa           16.0000000  84.0000000  16.0000000  0.0000000  78.6666667
Okobo                9.4594595  90.5405405   9.4594595  0.0000000  86.4864865
Okpe                38.4615385  61.5384615  38.4615385  0.0000000  69.2307692
Okpokwu             89.8305085  10.1694915  89.8305085  0.0000000  96.6101695
Okrika              33.3333333  50.0000000  50.0000000 16.6666667   0.0000000
Ola-oluwa           81.0810811  18.9189189  81.0810811  0.0000000 100.0000000
Olamabolo            0.0000000 100.0000000   0.0000000  0.0000000 100.0000000
Olorunda            67.4033149  32.5966851  67.4033149  0.0000000  37.0165746
Olorunsogo          62.6262626  37.3737374  62.6262626  0.0000000  48.4848485
Oluyole             86.4864865  13.5135135  86.4864865  0.0000000  55.4054054
Omala               48.6486486  51.3513514  48.6486486  0.0000000 100.0000000
Omumma              40.0000000  60.0000000  40.0000000  0.0000000 100.0000000
Ona-Ara             65.7894737  34.2105263  65.7894737  0.0000000  57.0175439
Ondo East           58.5185185  41.4814815  58.5185185  0.0000000  99.2592593
Ondo West           51.5527950  48.4472050  51.5527950  0.0000000  43.4782609
Onicha              89.9543379   4.1095890  95.8904110  5.9360731 100.0000000
Onitsha North       84.6153846  15.3846154  84.6153846  0.0000000   7.6923077
Onitsha South       50.0000000  50.0000000  50.0000000  0.0000000   0.0000000
Onna                19.0476190  80.9523810  19.0476190  0.0000000  63.4920635
Opobo/Nkoro         54.5454545  45.4545455  54.5454545  0.0000000  63.6363636
Oredo                4.3478261  95.6521739   4.3478261  0.0000000   0.0000000
Orelope             82.6530612  17.3469388  82.6530612  0.0000000  27.5510204
Orhionmwon           0.9009009  99.0990991   0.9009009  0.0000000 100.0000000
Ori Ire             84.9372385  15.0627615  84.9372385  0.0000000 100.0000000
Oriade              60.6177606  38.6100386  61.3899614  0.7722008  99.6138996
Orlu                41.1764706  58.8235294  41.1764706  0.0000000  31.3725490
Orolu               60.0000000  40.0000000  60.0000000  0.0000000  52.5000000
Oron                20.0000000  80.0000000  20.0000000  0.0000000  45.0000000
Orsu                25.0000000  75.0000000  25.0000000  0.0000000  25.0000000
Oru East            61.5384615  38.4615385  61.5384615  0.0000000  89.7435897
Oru West            65.8536585  34.1463415  65.8536585  0.0000000  58.5365854
Oruk Anam           22.0338983  77.9661017  22.0338983  0.0000000  98.3050847
Orumba North        68.1818182  31.8181818  68.1818182  0.0000000  89.3939394
Orumba South        48.4848485  51.5151515  48.4848485  0.0000000  48.4848485
Ose                 48.0000000  52.0000000  48.0000000  0.0000000 100.0000000
Oshimili North       3.0303030  96.9696970   3.0303030  0.0000000  81.8181818
Oshimili South       8.6956522  91.3043478   8.6956522  0.0000000  34.7826087
Oshodi-Isolo        38.2978723  61.7021277  38.2978723  0.0000000   0.0000000
Osisioma Ngwa       29.5454545  70.4545455  29.5454545  0.0000000  68.1818182
Osogbo              45.9302326  54.0697674  45.9302326  0.0000000  12.7906977
Oturkpo             87.0967742  12.9032258  87.0967742  0.0000000  98.3870968
Ovia North East      8.4337349  91.5662651   8.4337349  0.0000000  77.1084337
Ovia South West      5.1948052  94.8051948   5.1948052  0.0000000 100.0000000
Owan East            7.9365079  92.0634921   7.9365079  0.0000000 100.0000000
Owan West            6.8181818  93.1818182   6.8181818  0.0000000 100.0000000
Owerri-Municipal    41.9354839  58.0645161  41.9354839  0.0000000  16.1290323
Owerri North        27.9411765  72.0588235  27.9411765  0.0000000  64.7058824
Owerri West         50.8196721  49.1803279  50.8196721  0.0000000  91.8032787
Owo                 46.4088398  53.5911602  46.4088398  0.0000000  37.5690608
Oye                 61.9718310  28.8732394  71.1267606  9.1549296  76.0563380
Oyi                 38.3561644  61.6438356  38.3561644  0.0000000  41.0958904
Oyigbo              41.1764706  58.8235294  41.1764706  0.0000000 100.0000000
Oyo East            56.8181818  43.1818182  56.8181818  0.0000000  43.1818182
Oyo West            64.0625000  35.9375000  64.0625000  0.0000000  51.5625000
Oyun                71.6417910  28.3582090  71.6417910  0.0000000  89.5522388
Paikoro             89.3617021  10.6382979  89.3617021  0.0000000  69.1489362
Pankshin            91.7085427   8.2914573  91.7085427  0.0000000  94.4723618
Patani              27.2727273  72.7272727  27.2727273  0.0000000 100.0000000
Pategi              70.5454545  29.4545455  70.5454545  0.0000000  65.0909091
Port-Harcourt       69.5652174   5.2173913  94.7826087 24.3478261   2.6086957
Potiskum            25.0000000  75.0000000  25.0000000  0.0000000  51.3888889
Qua'an Pan          91.8840580   8.1159420  91.8840580  0.0000000  96.5217391
Rabah               58.7301587  41.2698413  58.7301587  0.0000000 100.0000000
Rafi                87.1212121  12.8787879  87.1212121  0.0000000 100.0000000
Rano                92.0000000   8.0000000  92.0000000  0.0000000  69.0000000
Remo North          43.1818182  56.8181818  43.1818182  0.0000000  63.6363636
Rijau               86.0927152  13.9072848  86.0927152  0.0000000  84.7682119
Rimi                71.4285714  28.5714286  71.4285714  0.0000000 100.0000000
Rimin Gado          97.5903614   1.8072289  98.1927711  0.6024096  99.3975904
Ringim              87.5000000  11.8055556  88.1944444  0.6944444  65.2777778
Riyom               89.1447368  10.8552632  89.1447368  0.0000000 100.0000000
Rogo                88.6486486  11.3513514  88.6486486  0.0000000  90.2702703
Roni                94.6666667   5.3333333  94.6666667  0.0000000 100.0000000
Sabon-Gari          76.2626263  23.7373737  76.2626263  0.0000000  26.5151515
Sabon Birni         80.5194805  19.4805195  80.5194805  0.0000000 100.0000000
Sabuwa              76.7441860  23.2558140  76.7441860  0.0000000  83.7209302
Safana              95.0310559   4.9689441  95.0310559  0.0000000 100.0000000
Sagbama             20.9677419  74.1935484  25.8064516  4.8387097  95.1612903
Sakaba              90.3448276   9.6551724  90.3448276  0.0000000  84.1379310
Saki East           58.7155963  41.2844037  58.7155963  0.0000000 100.0000000
Saki West           72.2972973  27.7027027  72.2972973  0.0000000  16.8918919
Sandamu             76.1467890  23.8532110  76.1467890  0.0000000 100.0000000
Sanga               94.1558442   5.8441558  94.1558442  0.0000000  95.4545455
Sapele               8.3333333  91.6666667   8.3333333  0.0000000  41.6666667
Sardauna            90.0000000  10.0000000  90.0000000  0.0000000  84.0000000
Shagamu             36.8852459  63.1147541  36.8852459  0.0000000  21.3114754
Shagari             17.3076923  82.6923077  17.3076923  0.0000000 100.0000000
Shanga              85.4368932  14.5631068  85.4368932  0.0000000 100.0000000
Shani               63.4146341  36.5853659  63.4146341  0.0000000 100.0000000
Shanono             77.1084337  22.8915663  77.1084337  0.0000000 100.0000000
Shelleng            88.8888889  11.1111111  88.8888889  0.0000000  88.8888889
Shendam             93.2242991   6.7757009  93.2242991  0.0000000  91.1214953
Shinkafi            88.5350318  11.4649682  88.5350318  0.0000000  78.3439490
Shira               89.7560976  10.2439024  89.7560976  0.0000000  89.2682927
Shiroro             90.8256881   9.1743119  90.8256881  0.0000000  95.4128440
Shomgom             88.8446215  11.1553785  88.8446215  0.0000000 100.0000000
Shomolu             21.4285714  78.5714286  21.4285714  0.0000000   0.0000000
Silame              46.6666667  53.3333333  46.6666667  0.0000000 100.0000000
Soba                91.7675545   8.2324455  91.7675545  0.0000000  93.7046005
Sokoto North        15.1515152  84.8484848  15.1515152  0.0000000   0.0000000
Sokoto South        32.0000000  68.0000000  32.0000000  0.0000000   0.0000000
Song                78.5714286  21.4285714  78.5714286  0.0000000  89.2857143
Southern Ijaw       64.5161290  35.4838710  64.5161290  0.0000000  96.7741935
Sule-Tankarkar      89.8969072  10.1030928  89.8969072  0.0000000 100.0000000
Suleja              80.4878049  19.5121951  80.4878049  0.0000000  19.5121951
Sumaila             96.7509025   3.2490975  96.7509025  0.0000000  94.9458484
Suru                80.0000000  20.0000000  80.0000000  0.0000000  90.0000000
Obi Benue           31.1475410  67.2131148  32.7868852  1.6393443   0.0000000
Surulere Lagos      58.2010582  41.7989418  58.2010582  0.0000000  94.7089947
Tafa                68.7500000  31.2500000  68.7500000  0.0000000  98.4375000
Tafawa-Balewa       87.5000000  12.5000000  87.5000000  0.0000000  92.9687500
Tai                  0.0000000 100.0000000   0.0000000  0.0000000 100.0000000
Takai               93.0337079   6.9662921  93.0337079  0.0000000  88.3146067
Takum               85.0000000  15.0000000  85.0000000  0.0000000  71.6666667
Talata Mafara       72.1925134  27.8074866  72.1925134  0.0000000  83.4224599
Tambuwal            48.3146067  51.6853933  48.3146067  0.0000000  83.1460674
Tangaza             56.2500000  43.7500000  56.2500000  0.0000000 100.0000000
Tarauni             88.0597015  10.4477612  89.5522388  1.4925373   0.0000000
Tarka               93.7500000   6.2500000  93.7500000  0.0000000  95.9821429
Tarmua               5.5555556  94.4444444   5.5555556  0.0000000  83.3333333
Taura               99.0977444   0.9022556  99.0977444  0.0000000 100.0000000
Tofa                89.1891892  10.8108108  89.1891892  0.0000000 100.0000000
Toro                94.7368421   5.2631579  94.7368421  0.0000000 100.0000000
Toto                37.6068376  62.3931624  37.6068376  0.0000000 100.0000000
Toungo              80.0000000  20.0000000  80.0000000  0.0000000 100.0000000
Tsafe               84.9246231  15.0753769  84.9246231  0.0000000  81.9095477
Tsanyawa            96.1290323   3.8709677  96.1290323  0.0000000  81.2903226
Tudun Wada          85.7988166  14.2011834  85.7988166  0.0000000  86.9822485
Tureta              58.5585586  41.4414414  58.5585586  0.0000000 100.0000000
Udenu               72.7272727  27.2727273  72.7272727  0.0000000  77.2727273
Udi                 71.4285714  28.5714286  71.4285714  0.0000000  80.0000000
Udu                 72.7272727  27.2727273  72.7272727  0.0000000  81.8181818
Udung Uko           11.1111111  88.8888889  11.1111111  0.0000000 100.0000000
Ughelli North       32.0754717  60.3773585  39.6226415  7.5471698  88.6792453
Ughelli South       32.0000000  68.0000000  32.0000000  0.0000000 100.0000000
Ugwunagbo           75.0000000  25.0000000  75.0000000  0.0000000  94.3181818
Uhunmwonde           7.9365079  92.0634921   7.9365079  0.0000000  95.2380952
Ukanafun            38.0952381  61.9047619  38.0952381  0.0000000  97.6190476
Ukum                89.8734177  10.1265823  89.8734177  0.0000000  94.9367089
Ukwa East           61.1111111  38.8888889  61.1111111  0.0000000 100.0000000
Ukwa West           45.1612903  54.8387097  45.1612903  0.0000000 100.0000000
Ukwuani             20.3703704  79.6296296  20.3703704  0.0000000  90.7407407
Umu-Nneochi         65.9090909  34.0909091  65.9090909  0.0000000  84.0909091
Umuahia North       52.7027027  47.2972973  52.7027027  0.0000000  40.5405405
Umuahia South       51.4563107  48.5436893  51.4563107  0.0000000  63.1067961
Ungogo              95.3642384   4.6357616  95.3642384  0.0000000  67.5496689
Unuimo              83.3333333  16.6666667  83.3333333  0.0000000  50.0000000
Uruan               10.4166667  85.4166667  14.5833333  4.1666667  97.9166667
Urue-Offong/Oruko   17.8571429  82.1428571  17.8571429  0.0000000  96.4285714
Ushongo             92.1397380   7.8602620  92.1397380  0.0000000 100.0000000
Ussa                84.3243243  15.6756757  84.3243243  0.0000000 100.0000000
Uvwie               36.3636364  63.6363636  36.3636364  0.0000000  18.1818182
Uyo                  5.0000000  95.0000000   5.0000000  0.0000000  67.5000000
Uzo-Uwani           92.0000000   8.0000000  92.0000000  0.0000000 100.0000000
Vandeikya           87.5000000  12.5000000  87.5000000  0.0000000  93.4523810
Wamako              35.8024691  64.1975309  35.8024691  0.0000000  86.4197531
Wamba               50.9803922  48.0392157  51.9607843  0.9803922 100.0000000
Warawa              97.4874372   2.5125628  97.4874372  0.0000000  98.4924623
Warji               98.5454545   1.4545455  98.5454545  0.0000000 100.0000000
Warri North          3.3333333  96.6666667   3.3333333  0.0000000  63.3333333
Warri South         55.7692308  44.2307692  55.7692308  0.0000000  28.8461538
Warri South West    42.8571429  57.1428571  42.8571429  0.0000000 100.0000000
Wasagu/Danko        76.0000000  24.0000000  76.0000000  0.0000000  77.1428571
Wase                72.4719101  27.5280899  72.4719101  0.0000000  79.7752809
Wudil               79.1666667  12.5000000  87.5000000  8.3333333  81.9444444
Wukari              93.4156379   6.5843621  93.4156379  0.0000000  67.9012346
Wurno               15.2542373  84.7457627  15.2542373  0.0000000  76.2711864
Wushishi            87.7697842  12.2302158  87.7697842  0.0000000 100.0000000
Yabo                70.7317073  29.2682927  70.7317073  0.0000000 100.0000000
Yagba East          54.2372881  45.7627119  54.2372881  0.0000000  86.4406780
Yagba West          68.0672269  31.9327731  68.0672269  0.0000000  72.2689076
Yakurr              76.6153846  23.3846154  76.6153846  0.0000000  60.3076923
Yala                81.7109145  18.2890855  81.7109145  0.0000000  94.9852507
Yamaltu/Deba        81.0483871  18.9516129  81.0483871  0.0000000  98.3870968
Yankwashi           97.3451327   2.6548673  97.3451327  0.0000000 100.0000000
Yauri               88.4615385  11.5384615  88.4615385  0.0000000  61.5384615
Yenegoa             25.9259259  74.0740741  25.9259259  0.0000000  88.8888889
Yola North          34.6153846  65.3846154  34.6153846  0.0000000  19.2307692
Yola South          69.2307692  30.7692308  69.2307692  0.0000000  30.7692308
Yorro               90.9547739   9.0452261  90.9547739  0.0000000  93.9698492
Yunusari             0.0000000 100.0000000   0.0000000  0.0000000  66.6666667
Yusufari            35.7142857  64.2857143  35.7142857  0.0000000 100.0000000
Zaki                94.3262411   5.6737589  94.3262411  0.0000000  90.7801418
Zango               52.3364486  47.6635514  52.3364486  0.0000000  83.1775701
Zango-Kataf         94.3444730   5.6555270  94.3444730  0.0000000 100.0000000
Zaria               94.9843260   5.0156740  94.9843260  0.0000000  29.1536050
Zing                88.9473684  11.0526316  88.9473684  0.0000000  85.2631579
Zurmi               79.8449612  20.1550388  79.8449612  0.0000000 100.0000000
Zuru                90.0000000  10.0000000  90.0000000  0.0000000  84.2857143

4.2.2 Trim High Correlation Variable and “shapeName”

cluster_varsTrim <- cluster_vars %>%
  select(-shapeName, -pct_ucN1000, -pct_mechPump)

4.2.2.1 review trimmed data table

summary(cluster_varsTrim)
 pct_functional   pct_nonFunctional  pct_unknown      pct_handPump   
 Min.   :  0.00   Min.   :  0.00    Min.   :  0.00   Min.   :  0.00  
 1st Qu.: 32.61   1st Qu.: 20.77    1st Qu.:  0.00   1st Qu.: 16.70  
 Median : 47.41   Median : 34.89    Median :  0.00   Median : 50.99  
 Mean   : 49.84   Mean   : 35.58    Mean   : 12.55   Mean   : 48.73  
 3rd Qu.: 66.99   3rd Qu.: 50.00    3rd Qu.: 20.83   3rd Qu.: 77.78  
 Max.   :100.00   Max.   :100.00    Max.   :100.00   Max.   :100.00  
  pct_tapStand       pct_uc300        pct_uc1000       pct_uc250      
 Min.   : 0.0000   Min.   :  0.00   Min.   :  0.00   Min.   : 0.0000  
 1st Qu.: 0.0000   1st Qu.: 38.67   1st Qu.: 12.20   1st Qu.: 0.0000  
 Median : 0.0000   Median : 65.91   Median : 31.27   Median : 0.0000  
 Mean   : 0.5794   Mean   : 60.17   Mean   : 37.54   Mean   : 0.6114  
 3rd Qu.: 0.0000   3rd Qu.: 87.02   3rd Qu.: 57.71   3rd Qu.: 0.0000  
 Max.   :32.8947   Max.   :100.00   Max.   :100.00   Max.   :32.8947  
   pct_urban0    
 Min.   :  0.00  
 1st Qu.: 57.27  
 Median : 86.45  
 Mean   : 72.71  
 3rd Qu.:100.00  
 Max.   :100.00

5. CLUSTERING ANALYSIS

5.1 Hierarchy Clustering

There are four (4) main steps :

  • compute proximity matrix.
  • assign data point to a cluster.
  • merge clusters based on similarity between clusters.
  • update the distance matrix.

5.1.1 Standardise Data

As shown in the 4.2.3.1, there are few variables with Max. different from others. Hence, standardisation will be required prior to further analysis.

5.1.1.1 standardise with min-max method

nga_wpStd <- normalize(cluster_varsTrim)
summary(nga_wpStd)
 pct_functional   pct_nonFunctional  pct_unknown      pct_handPump   
 Min.   :0.0000   Min.   :0.0000    Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.3261   1st Qu.:0.2077    1st Qu.:0.0000   1st Qu.:0.1670  
 Median :0.4741   Median :0.3489    Median :0.0000   Median :0.5099  
 Mean   :0.4984   Mean   :0.3558    Mean   :0.1255   Mean   :0.4873  
 3rd Qu.:0.6699   3rd Qu.:0.5000    3rd Qu.:0.2083   3rd Qu.:0.7778  
 Max.   :1.0000   Max.   :1.0000    Max.   :1.0000   Max.   :1.0000  
  pct_tapStand       pct_uc300        pct_uc1000       pct_uc250      
 Min.   :0.00000   Min.   :0.0000   Min.   :0.0000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.3867   1st Qu.:0.1220   1st Qu.:0.00000  
 Median :0.00000   Median :0.6591   Median :0.3127   Median :0.00000  
 Mean   :0.01761   Mean   :0.6017   Mean   :0.3754   Mean   :0.01859  
 3rd Qu.:0.00000   3rd Qu.:0.8702   3rd Qu.:0.5771   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.0000   Max.   :1.0000   Max.   :1.00000  
   pct_urban0    
 Min.   :0.0000  
 1st Qu.:0.5727  
 Median :0.8645  
 Mean   :0.7271  
 3rd Qu.:1.0000  
 Max.   :1.0000

5.1.1.2 standardise with Z-score method

nga_wpZ <- scale(cluster_varsTrim)
describe(nga_wpZ)
                  vars   n mean sd median trimmed  mad   min   max range  skew
pct_functional       1 774    0  1  -0.10   -0.02 1.04 -2.06  2.07  4.13  0.14
pct_nonFunctional    2 774    0  1  -0.03   -0.02 1.05 -1.71  3.10  4.81  0.23
pct_unknown          3 774    0  1  -0.62   -0.22 0.00 -0.62  4.30  4.92  2.01
pct_handPump         4 774    0  1   0.07    0.00 1.37 -1.49  1.57  3.06 -0.09
pct_tapStand         5 774    0  1  -0.19   -0.19 0.00 -0.19 10.46 10.65  7.22
pct_uc300            6 774    0  1   0.19    0.08 1.10 -2.02  1.34  3.35 -0.56
pct_uc1000           7 774    0  1  -0.21   -0.09 1.05 -1.28  2.14  3.42  0.61
pct_uc250            8 774    0  1  -0.20   -0.20 0.00 -0.20 10.37 10.57  7.10
pct_urban0           9 774    0  1   0.42    0.17 0.62 -2.23  0.84  3.06 -1.12
                  kurtosis   se
pct_functional       -0.62 0.04
pct_nonFunctional    -0.42 0.04
pct_unknown           4.15 0.04
pct_handPump         -1.33 0.04
pct_tapStand         58.65 0.04
pct_uc300            -0.87 0.04
pct_uc1000           -0.78 0.04
pct_uc250            57.14 0.04
pct_urban0           -0.09 0.04

5.1.1.3 visualise distribution of standardised clustering variable

-- functional water point

fwp <- ggplot(data=cluster_varsTrim, 
             aes(x= `pct_functional`)) +
  geom_histogram(bins=20, 
                 color="black", 
                 fill="steelblue") +
  ggtitle("Before Standardisation")


fwp_stdDf <- as.data.frame(nga_wpStd)
fwp_std <- ggplot(data=fwp_stdDf, 
       aes(x=`pct_functional`)) +
  geom_histogram(bins=20, 
                 color="black", 
                 fill="steelblue") +
  ggtitle("Min-Max Stdsn.")

fwp_zDf <- as.data.frame(nga_wpZ)
fwp_z <- ggplot(data=fwp_zDf, 
       aes(x=`pct_functional`)) +
  geom_histogram(bins=20, 
                 color="black", 
                 fill="steelblue") +
  ggtitle("Z-score Stdsn.")

ggarrange(fwp, fwp_std, fwp_z,
          ncol = 3,
          nrow = 1)

fwp <- ggplot(data=cluster_varsTrim, 
             aes(x= `pct_functional`)) +
  geom_density(color="black", 
                 fill="steelblue") +
  ggtitle("Before Standardisation")


fwp_stdDf <- as.data.frame(nga_wpStd)
fwp_std <- ggplot(data=fwp_stdDf, 
       aes(x=`pct_functional`)) +
  geom_density(color="black", 
                 fill="steelblue") +
  ggtitle("Min-Max Stdsn.")

fwp_zDf <- as.data.frame(nga_wpZ)
fwp_z <- ggplot(data=fwp_zDf, 
       aes(x=`pct_functional`)) +
  geom_density(color="black", 
                 fill="steelblue") +
  ggtitle("Z-score Stdsn.")

ggarrange(fwp, fwp_std, fwp_z,
          ncol = 3,
          nrow = 1)

-- water point deployed with handpump

HP <- ggplot(data=cluster_varsTrim, 
             aes(x= `pct_handPump`)) +
  geom_histogram(bins=20, 
                 color="black", 
                 fill="steelblue") +
  ggtitle("Before Standardisation")


fwp_stdDf <- as.data.frame(nga_wpStd)
HP_std <- ggplot(data=fwp_stdDf, 
       aes(x=`pct_handPump`)) +
  geom_histogram(bins=20, 
                 color="black", 
                 fill="steelblue") +
  ggtitle("Min-Max Stdsn.")

fwp_zDf <- as.data.frame(nga_wpZ)
HP_z <- ggplot(data=fwp_zDf, 
       aes(x=`pct_handPump`)) +
  geom_histogram(bins=20, 
                 color="black", 
                 fill="steelblue") +
  ggtitle("Z-score Stdsn.")

ggarrange(HP, HP_std, HP_z,
          ncol = 3,
          nrow = 1)

HP <- ggplot(data=cluster_varsTrim, 
             aes(x= `pct_handPump`)) +
  geom_density(color="black", 
                 fill="steelblue") +
  ggtitle("Before Standardisation")


fwp_stdDf <- as.data.frame(nga_wpStd)
HP_std <- ggplot(data=fwp_stdDf, 
       aes(x=`pct_handPump`)) +
  geom_density(color="black", 
                 fill="steelblue") +
  ggtitle("Min-Max Stdsn.")

fwp_zDf <- as.data.frame(nga_wpZ)
HP_z <- ggplot(data=fwp_zDf, 
       aes(x=`pct_handPump`)) +
  geom_density(color="black", 
                 fill="steelblue") +
  ggtitle("Z-score Stdsn.")

ggarrange(HP, HP_std, HP_z,
          ncol = 3,
          nrow = 1)

-- water point with 1000 users usage capacity

uc1000 <- ggplot(data=cluster_varsTrim, 
             aes(x= `pct_uc1000`)) +
  geom_histogram(bins=20, 
                 color="black", 
                 fill="steelblue") +
  ggtitle("Before Standardisation")

fwp_stdDf <- as.data.frame(nga_wpStd)
uc1000_std <- ggplot(data=fwp_stdDf, 
       aes(x=`pct_uc1000`)) +
  geom_histogram(bins=20, 
                 color="black", 
                 fill="steelblue") +
  ggtitle("Min-Max Stdsn.")

fwp_zDf <- as.data.frame(nga_wpZ)
uc1000_z <- ggplot(data=fwp_zDf, 
       aes(x=`pct_uc1000`)) +
  geom_histogram(bins=20, 
                 color="black", 
                 fill="steelblue") +
  ggtitle("Z-score Stdsn.")

ggarrange(uc1000, uc1000_std, uc1000_z,
          ncol = 3,
          nrow = 1)

uc1000 <- ggplot(data=cluster_varsTrim, 
             aes(x= `pct_uc1000`)) +
  geom_density(color="black", 
                 fill="steelblue") +
  ggtitle("Before Standardisation")


fwp_stdDf <- as.data.frame(nga_wpStd)
uc1000_std <- ggplot(data=fwp_stdDf, 
       aes(x=`pct_uc1000`)) +
  geom_density(color="black", 
                 fill="steelblue") +
  ggtitle("Min-Max Stdsn.")

fwp_zDf <- as.data.frame(nga_wpZ)
uc1000_z <- ggplot(data=fwp_zDf, 
       aes(x=`pct_uc1000`)) +
  geom_density(color="black", 
                 fill="steelblue") +
  ggtitle("Z-score Stdsn.")

ggarrange(uc1000, uc1000_std, uc1000_z,
          ncol = 3,
          nrow = 1)

5.1.2 Compute Proximity Matrix

Usage of the code chunk below :

dist( ) - stats - to compute the proximity distance matrix. Among euclidean, maximum, manhattan, canberra, binary and minkowski, euclidean is used to compute proxmat_euc.

proxmat_euc <- dist(cluster_varsTrim, method = 'euclidean')

5.1.3 Compute Hierarchical Clustering

Usage of the code chunk below :

hclust( ) - stats - to compute cluster with agglomeration method.

ggdendrogram( ) - ggdendro - to plot dendrogram with tools available in ggplot2.

hieClust_warD <- hclust(proxmat_euc, method = 'ward.D')
ggdendrogram(hieClust_warD, 
             rotate = TRUE, 
             size = 2, 
             theme_dendro = FALSE)

5.1.4 Determine Optimal Clustering Algorithm

Usage of the code chunk below :

agnes( ) - cluster - to get agglomerative coefficient of 4 clustering structure, namely “average”, “single”, “complete” and “ward”.

m <- c( "average", "single", "complete", "ward")

names(m) <- c( "average", "single", "complete", "ward")

ac <- function(x) {
  agnes(cluster_varsTrim, method = x)$ac
  }

map_dbl(m, ac)
  average    single  complete      ward 
0.9264460 0.8825086 0.9494033 0.9923235

Remarks :

  • Value 1 indicate strongest clustering structure.

  • Ward’s method provides the strongest clustering structure. Therefore, Ward’s method to be used in subsequent analysis.

5.1.5 Determine Optimal Clusters

To determine the optimal clusters to retain, following commons methods are tested :

  • Gap statistic

  • Elbow

  • Average Silhouette

5.1.5.1 compute Gap Statistic method

Usage of the code chunk below :

clusGap( ) - cluster - to compute the gap statistic.

set.seed(12345)
gap_stat <- clusGap(cluster_varsTrim, 
                    FUN = hcut, 
                    nstart = 25, 
                    K.max = 30, 
                    B = 50)
# Print the result
print(gap_stat, method = "firstmax")
Clustering Gap statistic ["clusGap"] from call:
clusGap(x = cluster_varsTrim, FUNcluster = hcut, K.max = 30, B = 50, nstart = 25)
B=50 simulated reference sets, k = 1..30; spaceH0="scaledPCA"
 --> Number of clusters (method 'firstmax'): 30
          logW    E.logW       gap      SE.sim
 [1,] 9.815280 10.315513 0.5002330 0.008043258
 [2,] 9.602465 10.203144 0.6006791 0.008510149
 [3,] 9.510126 10.144007 0.6338806 0.010041659
 [4,] 9.443727 10.094054 0.6503272 0.009408325
 [5,] 9.337773 10.055036 0.7172630 0.008377048
 [6,] 9.286151 10.021049 0.7348986 0.008061617
 [7,] 9.226030  9.992116 0.7660864 0.007509762
 [8,] 9.181055  9.967079 0.7860239 0.007406854
 [9,] 9.132662  9.944999 0.8123364 0.007505962
[10,] 9.088576  9.924941 0.8363644 0.008003248
[11,] 9.057435  9.906340 0.8489044 0.008086779
[12,] 9.019733  9.888898 0.8691655 0.008378669
[13,] 8.988798  9.872948 0.8841499 0.008499708
[14,] 8.962331  9.857905 0.8955736 0.008609360
[15,] 8.932159  9.843557 0.9113980 0.008574353
[16,] 8.908477  9.830133 0.9216564 0.008442998
[17,] 8.880801  9.817233 0.9364313 0.008235439
[18,] 8.846775  9.805149 0.9583744 0.008068772
[19,] 8.828254  9.793671 0.9654167 0.007975742
[20,] 8.812263  9.782502 0.9702393 0.007904523
[21,] 8.793736  9.771938 0.9782012 0.007903082
[22,] 8.777957  9.761659 0.9837022 0.007927244
[23,] 8.762944  9.751686 0.9887413 0.007838986
[24,] 8.745719  9.741903 0.9961837 0.007850884
[25,] 8.732706  9.732508 0.9998020 0.007792150
[26,] 8.716858  9.723358 1.0064996 0.007813310
[27,] 8.703095  9.714414 1.0113191 0.007684052
[28,] 8.684688  9.705770 1.0210819 0.007586041
[29,] 8.664250  9.697408 1.0331579 0.007592467
[30,] 8.649964  9.689233 1.0392698 0.007607051

-- visualise gap_stat

Usage of the code chunk below :

fviz_nbclust( ) - factoextra - to compute and visualise the Optimal Number of clusters.

set.seed(12345)
fviz_nbclust(nga_wpZ, 
             kmeans, 
             nstart = 25,  
             method = "gap_stat", 
             nboot = 50)+
  labs(subtitle = "Gap statistic method")
Warning: did not converge in 10 iterations

Warning: did not converge in 10 iterations

Warning: did not converge in 10 iterations

5.1.5.2 compute and visualise Elbow method

fviz_nbclust(nga_wpZ, kmeans, method = "wss") +
    geom_vline(xintercept = 4, linetype = 2)+
  labs(subtitle = "Elbow method")

5.1.5.3 compute and visualise Silhouette method

fviz_nbclust(nga_wpZ, kmeans, method = "silhouette")+
  labs(subtitle = "Silhouette method")

Remarks :

Given the Elbow method, Silhouette method and Gap Statistic method, the 5-cluster by Silhouette method will be used for the rest of the study.

5.1.5.4 interpret with Dendrogram

Usage of the code chunk below :

rect.hclust( ) - stats - to draw the dendrogram with a border around the selected clusters.

plot(hieClust_warD, cex = 0.6)
rect.hclust(hieClust_warD, 
            k = 5, 
            border = 2:5)

5.1.6 Visually-Driven Hierarchical Clustering Analysis

The data is loaded into a data frame, but it has to be a data matrix to plot the heatmap. Hence, the data frame will need to first transform into a matrix.

5.1.6.1 transform data frame into matrix

Usage of the code chunk below :

data.matrix( ) - base - to transform cluster_varsTrim data frame into a data matrix, and named it as nga_clustMat.

nga_clustMat <- data.matrix(cluster_varsTrim)

5.1.6.2 plot interactive cluster heatmap

Usage of the code chunk below :

heatmaply( ) - heatmaply - to build an interactive cluster heatmap.

heatmaply(normalize(nga_clustMat),
          Colv=NA,
          dist_method = "euclidean",
          hclust_method = "ward.D",
          seriate = "OLO",
          colors = Blues,
          k_row = 5,
          margins = c(NA,200,60,NA),
          fontsize_row = 4,
          fontsize_col = 5,
          main="Geographic Segmentation of Nigeria by Water Points",
          xlab = "Water Points",
          ylab = "Nigeria LGA"
          )

Remarks :

Based on the plot above, 5 clusters to be retained for further analysis.

5.1.6.3 map the formed cluster

Usage of the code chunk below :

cutree( ) - base - to derive a 5-cluster model, and named the output as groups.

groups <- as.factor(cutree(hieClust_warD, k=5))

5.1.6.4 append groups to wp_ngaTrans

nga_clust.sf <- cbind(wp_ngaTrans, as.matrix(groups)) %>%
  rename(`cluster`=`as.matrix.groups.`)

5.1.6.5 plot choropleth map :: nga_clust.sf

qtm(nga_clust.sf, "cluster")

Remarks :

The choropleth map above shows the fragmented clusters by the used of non-spatial clustering algorithm (hierarchical cluster analysis method).

5.2 Spatially Constrained Clustering :: SKATER Approach

SKATER function only support sp objects in SpatialPolygonDataFrame. Hence, the wp_ngaTrans has to first transform into SpatialPolygonDataFrame before proceed further.

5.2.1 Convert SF to SP Data Frame

Usage of the code chunk below :

as_Spatial( ) - sf - to convert wp_ngaTrans into nga_sp in a SP data frame.

nga.sp <- as_Spatial(wp_ngaTrans)

5.2.2 Compute Neighbour List

Usage of the code chunk below :

poly2nb( ) - spdep - to compute the neighbours list from polygon list.

nga.nb <- poly2nb(nga.sp, queen = TRUE)
summary(nga.nb)
Neighbour list object:
Number of regions: 774 
Number of nonzero links: 4440 
Percentage nonzero weights: 0.7411414 
Average number of links: 5.736434 
1 region with no links:
86
Link number distribution:

  0   1   2   3   4   5   6   7   8   9  10  11  12  14 
  1   2  14  57 125 182 140 122  72  41  12   4   1   1 
2 least connected regions:
138 560 with 1 link
1 most connected region:
508 with 14 links

Remarks :

There is one (1) region, i.e. #86 is without link. It has to be removed first before proceed to plot the neighbours list.

5.2.2.1 remove 0-neighbour region

wp_ngaTrans1 <- wp_ngaTrans[-86,]
cluster_varsTrim1 <- cluster_varsTrim[-86,]
nga_clust.sf1 <- nga_clust.sf[-86,]
nga_wpZ1 <- nga_wpZ[-86,]
nga.sp1 <- as_Spatial(wp_ngaTrans1)

nga.nb1 <- poly2nb(nga.sp1)
summary(nga.nb1)
Neighbour list object:
Number of regions: 773 
Number of nonzero links: 4440 
Percentage nonzero weights: 0.7430602 
Average number of links: 5.743855 
Link number distribution:

  1   2   3   4   5   6   7   8   9  10  11  12  14 
  2  14  57 125 182 140 122  72  41  12   4   1   1 
2 least connected regions:
138 560 with 1 link
1 most connected region:
508 with 14 links

5.2.2.2 plot Neighbour List by Centroid Node

Usage of the code chunk below : plot the boundary first before the neighbour list object to avoid any region from being clipped away.

plot(nga.sp1, 
     border=grey(.5))
plot(nga.nb1, 
     coordinates(nga.sp1), 
     col="blue", 
     add=TRUE)

5.2.3 Compute Minimum Spanning Tree (MST)

5.2.3.1 calculate edge costs

Usage of the code chunk below :

nbcosts( ) - spdep - to compute the cost of each edge which is the distance between nodes.

edge_cost <- nbcosts(nga.nb1, cluster_varsTrim1)

5.2.3.2 specify spatial weight

nb2listw( ) - spdep - to specify edge_cost as the spatial weights. Set the “style” to “B” to ensure the cost values are not row-standardised.

nga.w <- nb2listw(nga.nb1,
                  edge_cost,
                  style = "B")
Warning in nb2listw(nga.nb1, edge_cost, style = "B"): zero sum general weights
summary(nga.w)
Characteristics of weights list object:
Neighbour list object:
Number of regions: 773 
Number of nonzero links: 4440 
Percentage nonzero weights: 0.7430602 
Average number of links: 5.743855 
Link number distribution:

  1   2   3   4   5   6   7   8   9  10  11  12  14 
  2  14  57 125 182 140 122  72  41  12   4   1   1 
2 least connected regions:
138 560 with 1 link
1 most connected region:
508 with 14 links

Weights style: B 
Weights constants summary:
    n     nn       S0       S1        S2
B 773 597529 245120.7 38463020 406298492

5.2.3.3 compute minimum spanning tree

Usage of the code chunk below :

nbcosts( ) - spdep - to compute the minimum spanning tree.

nga_minSpanT <- mstree(nga.w)

-- review class and dimension of the computed MST

class(nga_minSpanT)
[1] "mst"    "matrix"
dim(nga_minSpanT)
[1] 772   3
head(nga_minSpanT)
     [,1] [,2]      [,3]
[1,]  474  387 134.66287
[2,]  387  478  64.55618
[3,]  387  439  78.95255
[4,]  439  476  50.91751
[5,]  439  270 105.68114
[6,]  270   90  66.67241

5.2.3.4 plot MST Neighbour List

plot(nga.sp1, border=gray(.5))
plot.mst(nga_minSpanT,
         coordinates(nga.sp1), 
         col="blue", 
         cex.lab=0.7, 
         cex.circles=0.005, 
         add=TRUE)

5.2.4 Compute Spatially Constrained Cluster

Usage of the code chunk below :

skater( ) - spdep - to compute the spatially constrained cluster.

clust5 <- spdep::skater(edges = nga_minSpanT[,1:2],
                        data = cluster_varsTrim1,
                        method = "euclidean",
                        ncuts = 4)
str(clust5)
List of 8
 $ groups      : num [1:773] 3 3 1 5 4 1 2 2 1 3 ...
 $ edges.groups:List of 5
  ..$ :List of 3
  .. ..$ node: num [1:309] 773 747 492 131 382 224 413 488 439 257 ...
  .. ..$ edge: num [1:308, 1:3] 131 382 224 413 257 767 439 704 476 75 ...
  .. ..$ ssw : num 17013
  ..$ :List of 3
  .. ..$ node: num [1:129] 597 315 316 557 195 571 339 744 205 213 ...
  .. ..$ edge: num [1:128, 1:3] 315 316 557 195 571 15 82 579 744 351 ...
  .. ..$ ssw : num 7874
  ..$ :List of 3
  .. ..$ node: num [1:85] 364 10 729 215 337 551 102 103 66 19 ...
  .. ..$ edge: num [1:84, 1:3] 23 536 578 103 19 375 727 617 188 103 ...
  .. ..$ ssw : num 4545
  ..$ :List of 3
  .. ..$ node: num [1:39] 550 202 330 287 374 732 537 586 733 201 ...
  .. ..$ edge: num [1:38, 1:3] 612 586 136 245 332 429 504 537 586 616 ...
  .. ..$ ssw : num 1294
  ..$ :List of 3
  .. ..$ node: num [1:211] 67 510 401 122 24 526 475 489 663 303 ...
  .. ..$ edge: num [1:210, 1:3] 67 549 510 119 639 401 556 122 693 70 ...
  .. ..$ ssw : num 10380
 $ not.prune   : NULL
 $ candidates  : int [1:5] 1 2 3 4 5
 $ ssto        : num 52660
 $ ssw         : num [1:5] 52660 48724 44268 42679 41106
 $ crit        : num [1:2] 1 Inf
 $ vec.crit    : num [1:773] 1 1 1 1 1 1 1 1 1 1 ...
 - attr(*, "class")= chr "skater"

5.2.4.1 tabulate cluster assignment

ccs5 <- clust5$groups
table(ccs5)
ccs5
  1   2   3   4   5 
309 129  85  39 211

5.2.4.2 plot the pruned tree

plot(nga.sp1, border=gray(.5))
plot(clust5, 
     coordinates(nga.sp1), 
     cex.lab=.7,
     groups.colors=c("red","green","blue", "brown", "pink"),
     cex.circles=0.005, 
     add=TRUE)
Warning in segments(coords[id1, 1], coords[id1, 2], coords[id2, 1],
coords[id2, : "add" is not a graphical parameter

Warning in segments(coords[id1, 1], coords[id1, 2], coords[id2, 1],
coords[id2, : "add" is not a graphical parameter

Warning in segments(coords[id1, 1], coords[id1, 2], coords[id2, 1],
coords[id2, : "add" is not a graphical parameter

Warning in segments(coords[id1, 1], coords[id1, 2], coords[id2, 1],
coords[id2, : "add" is not a graphical parameter

Warning in segments(coords[id1, 1], coords[id1, 2], coords[id2, 1],
coords[id2, : "add" is not a graphical parameter

5.2.5 Visualise SKATER Clusters in Choropleth Map

groups_mat <- as.matrix(clust5$groups)

nga_spClust.sf <- cbind(nga_clust.sf1, as.factor(groups_mat)) %>%
  rename(`sp_cluster`=`as.factor.groups_mat.`)

To compare the output of hierarchical clustering and spatially constrained hierarchical clustering :

hieClust_map <- qtm(nga_clust.sf1,
                  "cluster") + 
  tm_borders(alpha = 0.5) 

ngaClust_map <- qtm(nga_spClust.sf,
                   "sp_cluster") + 
  tm_borders(alpha = 0.5) 

tmap_arrange(hieClust_map, ngaClust_map,
             asp=NA, ncol=2)
Warning: One tm layer group has duplicated layer types, which are omitted. To
draw multiple layers of the same type, use multiple layer groups (i.e. specify
tm_shape prior to each of them).

Warning: One tm layer group has duplicated layer types, which are omitted. To
draw multiple layers of the same type, use multiple layer groups (i.e. specify
tm_shape prior to each of them).

5.3 Spatially Constrained Clustering :: ClustGeo Method

5.3.1 Perform Ward-like Hierarchical Clustering

Usage of the code chunk below :

hclustgeo( ) - ClustGeo - to perform a typical Ward-like hierarchical clustering.

proxmat_ngc <- dist(cluster_varsTrim1, method = 'euclidean')
nonGeo_clust <- hclustgeo(proxmat_ngc)
plot(nonGeo_clust, cex = 0.5)
rect.hclust(nonGeo_clust, 
            k = 5, 
            border = 2:5)

5.3.1.1 visualise the formed clusters

groups_ngc <- as.factor(cutree(nonGeo_clust, k=5))
nga_ngeo_clust.sf <- cbind(wp_ngaTrans1, as.matrix(groups_ngc)) %>%
  rename(`cluster` = `as.matrix.groups_ngc.`)
qtm(nga_ngeo_clust.sf, "cluster")

5.3.2 Perform Spatially Constrained Hierarchical Clustering

Usage of the code chunk below :

st_distance( ) - sf - to derive the spatial distance matrix before perform spatially constrained hierarchical clustering.

as.dist( ) - stats - to convert the data frame into matrix.

dist <- st_distance(wp_ngaTrans1, wp_ngaTrans1)
dist_mat <- as.dist(dist)

5.3.2.1 determine alpha value

choicealpha( ) - psych - to determine a suitable value for the mixing parameter alpha.

cr <- choicealpha(
  proxmat_ngc, 
  dist_mat, 
  range.alpha = seq(0, 1, 0.1), 
  K=5, 
  graph = TRUE)

Remarks :

With reference to the plot above, alpha = 0.4 to be used to perform spatially constrained hierarchical clustering.

5.3.2.2 compute spatially constrained hierarchical clustering

clustG <- hclustgeo(proxmat_ngc, 
                    dist_mat, 
                    alpha = 0.4)

5.3.2.3 derive cluster object

groups_cg <- as.factor(cutree(clustG, k=5))

5.3.2.4 combine group_cg with wp_ngaTrans1

wp_nga1_GClust <- cbind(wp_ngaTrans1, as.matrix(groups_cg)) %>%
  rename(`cluster` = `as.matrix.groups_cg.`)

5.3.2.5 plot delineated spatially constrained cluster

qtm(wp_nga1_GClust, "cluster")


5.4 Visual Interpretation of Clusters

5.4.1 Visualise Individual Clustering Variable

5.4.1.1 plot boxplot

ggplot(data = nga_ngeo_clust.sf,
       aes(x = cluster, y = pct_functional)) +
  geom_boxplot()

Remarks :

The boxplot reveals Cluster 5 displays the highest mean of functional water points. This is followed by Cluster 1, 3, 2, and 4.

5.4.2 Visualise Multivariate

Usage of the code chunk below :

ggparcoord( ) - GGally - to reveal clustering variables by cluster.

nga_ngeo_clust.sf1 <- nga_ngeo_clust.sf %>%
  select("shapeName", 
         "pct_functional", 
         "pct_nonFunctional", 
         "pct_unknown", 
         "pct_handPump", 
         "pct_tapStand", 
         "pct_uc300", 
         "pct_uc1000", 
         "pct_uc250", 
         "pct_urban0",
         "cluster")
         
head(nga_ngeo_clust.sf1,3)
Simple feature collection with 3 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 7.307433 ymin: 5.052192 xmax: 13.83477 ymax: 13.71406
Projected CRS: Minna / Nigeria West Belt
  shapeName pct_functional pct_nonFunctional pct_unknown pct_handPump
1 Aba North       41.17647          52.94118    5.882353    11.764706
2 Aba South       40.84507          46.47887    9.859155     9.859155
3    Abadam        0.00000           0.00000    0.000000     0.000000
  pct_tapStand pct_uc300 pct_uc1000 pct_uc250 pct_urban0 cluster
1            0  17.64706   82.35294         0   0.000000       1
2            0  12.67606   87.32394         0   5.633803       1
3            0   0.00000    0.00000         0   0.000000       1
                        geometry
1 MULTIPOLYGON (((7.401109 5....
2 MULTIPOLYGON (((7.334479 5....
3 MULTIPOLYGON (((13.83477 13...
ggparcoord(data = nga_ngeo_clust.sf,
           columns = c(2:19),
           scale = "globalminmax",
           alphaLines = 0.2,
           boxplot = TRUE, 
           title = "Multiple Parallel Coordinates Plots of Variables by Cluster") +
  facet_grid(~ cluster, scales = "fixed") + 
  theme(axis.text.x = element_text(angle = 30))

The parallel coordinate plot above reveals that households in Cluster 4 townships tend to own the highest number of TV and mobile-phone. On the other hand, households in Cluster 5 tends to own the lowest of all the five ICT.

Note that the scale argument of ggparcoor() provide several methods to scale the clustering variables. They are:

  • std: univariately, subtract mean and divide by standard deviation.

  • robust: univariately, subtract median and divide by median absolute deviation.

  • uniminmax: univariately, scale so the minimum of the variable is zero, and the maximum is one.

  • globalminmax: no scaling is done; the range of the graphs is defined by the global minimum and the global maximum.

  • center: use uniminmax to standardize vertical height, then center each variable at a value specified by the scaleSummary param.

  • centerObs: use uniminmax to standardize vertical height, then center each variable at the value of the observation specified by the centerObsID param

5.4.3 Compute Summary Statistics

nga_ngeo_clust.sf %>% 
  st_set_geometry(NULL) %>%
  group_by(cluster) %>%
  summarise(mean_pct_functional = mean(pct_functional),
            mean_pct_nonFunctional = mean(pct_nonFunctional),
            mean_pct_unknown = mean(pct_unknown),
            mean_pct_handPump = mean(pct_handPump), 
            mean_pct_tapStand = mean(pct_tapStand), 
            mean_pct_uc300 = mean(pct_uc300), 
            mean_pct_uc1000 = mean(pct_uc1000), 
            mean_pct_uc250 = mean(pct_uc250), 
            mean_pct_urban0 = mean(pct_urban0))
# A tibble: 5 × 10
  cluster mean_pct_fun…¹ mean_…² mean_…³ mean_…⁴ mean_…⁵ mean_…⁶ mean_…⁷ mean_…⁸
  <chr>            <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
1 1                 51.7    29.0   9.25    35.0   0.525     44.1    45.5  0.539 
2 2                 46.2    42.2  11.3     59.9   1.00      70.6    28.3  1.03  
3 3                 40.5    49.4   9.15     9.87  0.304     19.2    80.4  0.345 
4 4                 18.4    14.6  67.0     18.1   0.337     72.1    27.5  0.410 
5 5                 78.9    20.7   0.295   88.7   0.0677    89.2    10.7  0.0903
# … with 1 more variable: mean_pct_urban0 <dbl>, and abbreviated variable names
#   ¹​mean_pct_functional, ²​mean_pct_nonFunctional, ³​mean_pct_unknown,
#   ⁴​mean_pct_handPump, ⁵​mean_pct_tapStand, ⁶​mean_pct_uc300, ⁷​mean_pct_uc1000,
#   ⁸​mean_pct_uc250